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class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">直接回答\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>AI辅助预取有可能减少一些与内存相关的卡顿，但它不会是一个通用的防卡顿按钮。\u003C\u002Fstrong>研究已经表明，深度学习预取器可以改善CPU-GPU统一内存的页面预测。游戏引擎可以添加那些基准测试没有的东西：明确的世界和玩家状态。Figure Rocks的假设是将两者结合起来。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Figure Rocks假设，而非已宣布的功能\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">本文中的术语\u003Cstrong>预测性游戏内存\u003C\u002Fstrong>描述了一种提议的架构：确定性引擎知识 + 学习到的访问预测 + 预取 + 驻留策略。它不是NVIDIA、Microsoft、Epic或AMD的产品名称。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"目录\">\u003Cstrong class=\"editorjs-toc__title\">目录\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">首先将内存卡顿与其他卡顿区分开\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-8\" class=\"editorjs-toc__link\">预取已经存在；新问题是如何预测\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-11\" class=\"editorjs-toc__link\">已有研究证据表明学习型GPU预取可行\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-16\" class=\"editorjs-toc__link\">游戏拥有通用预取器没有的额外信息\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">引擎可以将已知的未来与预测的未来相结合\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">一个简单示例：跑向一扇门\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-23\" class=\"editorjs-toc__link\">为什么随着神经渲染的发展，这可能更加重要\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-26\" class=\"editorjs-toc__link\">失败模式与机遇同样重要\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-29\" class=\"editorjs-toc__link\">如何在真实引擎中测试它\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-31\" class=\"editorjs-toc__link\">这个假设的局限\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">与通用 AI 架构的轻微联系\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-36\" class=\"editorjs-toc__link\">主要来源\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">首先将内存卡顿与其他卡顿区分开\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">看起来相似的卡顿来源\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">内存\u002F数据路径\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">其他路径\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">快速内存中缺少资源\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">可能\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">存储或解压缩也可能涉及\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">着色器未就绪\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">可能涉及缓存\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">编译\u002F管线创建可能是真正原因\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">CPU模拟峰值\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">缓存\u002F内存行为可能有贡献\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">游戏逻辑或绘制调用工作可能占主导\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">在线卡顿\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">通常不是主要原因\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">服务器或数据包时序可能占主导\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">驱逐\u002F驻留压力\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">直接相关\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">仅从分配计量器可能误诊\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fzh\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">为什么PC游戏在编译着色器时会卡顿——以及高级着色器交付如何改变它\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">着色器卡顿是与内存页面预测不同的问题，不应被归为同一种诊断。\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">将着色器卡顿与内存卡顿区分开 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-8\">预取已经存在；新问题是如何预测\u003C\u002Fh2>\n\u003Cp>传统预取试图在处理器明确请求数据之前移动数据。硬件预取器利用访问模式。引擎在玩家到达世界单元格之前流式传输它们。操作系统和运行时保持可能需要的页面热。困难的部分是错误预测：预取太少仍然会卡顿；预取太多会浪费带宽并驱逐有用数据。\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">基本的预取权衡\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">观察\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">收集最近的访问、世界状态和工作负载上下文。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">预测\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">估计接下来需要哪些页面\u002F资源\u002F着色器。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">移动\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">在需求之前传输或使这些资源驻留。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">使用\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">如果预测正确，GPU无需等待即可消耗资源。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">纠正\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">驱逐错误预测并更新模型\u002F策略。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-11\">已有研究证据表明学习型GPU预取可行\u003C\u002Fh2>\n\u003Cp>一项关于基于深度学习的CPU-GPU统一虚拟内存预取的已发表研究使用Transformer建模全局内存访问上下文，然后推导出更轻量的预测器。在11个内存密集型基准测试中，论文报告相比对比框架性能提升10.89%，设备内存页面命中率为89.02%对76.10%，CPU-GPU互连流量降低11.05%。\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">研究结果\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">报告值\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">支持什么\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">不证明什么\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">学习型UVM性能\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">+10.89%\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">学习型访问预测可以超越先前的UVM预取基线\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">商业游戏获得10.89% FPS提升\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">UVM页面命中率\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">89.02% vs 76.10%\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">预测可以实质性地改变请求数据所在位置\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">每个游戏纹理\u002F页面都同样可预测\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">CPU–GPU互连流量\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">−11.05%\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">更好的放置可以减少反应性移动\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">游戏中PCIe流量总是下降\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">AAAI注意力内核效率\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">2.15×\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">预取可以将昂贵的内存访问隐藏在有用的计算后面\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">游戏内核获得2.15×\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">AAAI端到端吞吐量\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">高达1.97×\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">延迟隐藏可以传播到单个内核之外\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">1.97×游戏帧率倍增器\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Caside class=\"editorjs-callout editorjs-callout--warning my-6 rounded-xl border p-5 border-amber-300 bg-amber-50 dark:border-amber-900 dark:bg-amber-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">不要将这些数字复制到游戏上\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">这些结果来自论文的基准测试集，\u003Cstrong>而非商业游戏引擎\u003C\u002Fstrong>。它们证明学习型内存访问预测可以在GPU UVM工作负载中起作用。它们不证明游戏中10.89%的FPS提升或特定的卡顿减少。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cp>学习型UVM研究有用，因为它从内存行为预测页面访问可能是什么。2026年AAAI KV缓存工作有用的原因不同：它表明调度器可以利用原本空闲的带宽窗口来决定何时在需求之前将数据移入更快的缓存。它们共同支持Figure Rocks假设中的两个独立机制——预测和时机——尽管两篇论文都不是游戏基准测试。\u003C\u002Fp>\n\u003Ch2 id=\"section-16\">游戏拥有通用预取器没有的额外信息\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">预测性游戏内存循环\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. 读取确定性状态\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">摄像机方向、位置、速度、关卡图、可见性、动画、任务或遭遇状态。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. 读取访问历史\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">最近使用了哪些纹理页面、网格、着色器、几何集群和模型数据。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. 预测近期工作集\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">根据成本估计未来几帧或几秒可能需要的资源。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. 分配置信度和成本\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">95%可能的4 MB资源与20%可能的2 GB区域是不同的决策。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. 在带宽预算内预取\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">不要让预测本身饱和当前渲染所需的路径。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">6. 测量未命中和污染\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">跟踪避免的卡顿、错误预测和被驱逐的有用数据。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">7. 适应\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">根据实际场景和玩家行为更新预测器或引擎策略。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">有趣的混合方案\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">游戏不必在手工编写的流式传输和AI之间选择。\u003Cstrong>最强的设计可能是混合的：\u003C\u002Fstrong>确定性规则保证关键资源，而学习型预测器对可选或近期工作进行排序，选择预取深度，并只花费安全的带宽余量。如果置信度低，系统回退到编写的规则。\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-19\">引擎可以将已知的未来与预测的未来相结合\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">确定性知识与学习型预测\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">引擎已知\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">预测器可估计\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">相机 \u002F 传送门状态\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">当前视图、视锥体、房间图、可见传送门\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">玩家接下来可能转向的方向\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">玩家移动\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">速度、输入、导航约束\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">未来几个时刻可能的行进路径\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">资产依赖\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">已知关卡单元或对象的依赖关系\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">哪些可选邻近资产将变为热点\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">LOD 状态\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">当前距离和阈值\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">哪些 LOD 转换可能很快发生\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">材质\u002F着色器图\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">已知可见内容所需的着色器\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">接下来可能请求哪些变体\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">核心优势\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">掌握硬性关卡规则、依赖关系和保证的转换\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">根据行为和上下文对模糊的未来访问进行排序\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">失败模式\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">可能过于保守并加载大量集合\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">可能出错并污染缓存或内存\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">最佳用途\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">关键资产和保证的转换\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">在有界带宽预算内进行可选优先级排序\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">安全策略\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">易于推理和测试\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">需要置信度阈值、遥测和确定性回退\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-21\">一个简单示例：跑向一扇门\u003C\u002Fh2>\n\u003Cp>想象一个玩家沿着走廊冲刺，跑向一扇通向密集集市广场的门。传统的流式系统已经知道下一个区域，并可以开始加载它。预测性内存增加了第二层：基于相机方向、移动、先前的玩家选择和当前任务状态，它可以对哪些纹理、NPC 动画数据、着色器和几何体子集最有可能首先被需要进行排序。\u003C\u002Fp>\n\u003Ch2 id=\"section-23\">为什么随着神经渲染的发展，这可能更加重要\u003C\u002Fh2>\n\u003Cp>神经图形增加了新的数据类别：模型权重、潜在或压缩表示、历史缓冲区和中间张量。如果这些资源与纹理和几何体竞争，驻留管理将变得更加复杂。预测性调度不仅可以决定移动哪个资产，还可以决定保留哪种表示：原始数据、压缩的神经表示或可重建的形式。\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Fzh\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">DLSS 5 不仅仅是超分辨率：生成式神经渲染实际改变了什么\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">神经渲染改变了帧所需的数据以及需要的时间。\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">阅读神经渲染分析 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-26\">失败模式与机遇同样重要\u003C\u002Fh2>\n\u003Cp>预取不是免费的。错误的预测可能会获取玩家永远不会看到的资源，驱逐有用的页面，并与帧关键流量竞争。因此，正确的目标不是最大预取；而是在有界带宽和驻留预算内实现最大有用命中率。\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">失败模式\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">会发生什么\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">缓解思路\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">错误预测\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">预取了错误的数据\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">置信度阈值；为确定性需求预留预算\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">缓存污染\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">有用的当前数据被驱逐\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">分离预测驻留类和受保护驻留类\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">带宽争用\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">预取从当前帧窃取带宽\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">仅在余量窗口调度；强制执行每帧预算\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">行为变化\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">玩家转身或快速旅行\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">低成本取消和快速回退\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">模型开销\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">预测器成本高于节省\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">小模型、离线训练或规则衍生的轻量预测器\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">非内存卡顿\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">问题仍然存在\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">保持诊断分离：着色器、CPU、网络和存储路径需要各自的测试\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-29\">如何在真实引擎中测试它\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">预测性内存评估\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">A. 建立基线\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">在没有预测的情况下捕获帧时间尖峰、页面错误\u002F迁移、驻留未命中和数据传输量。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">B. 仅添加确定性预取\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">测量传统引擎知识已经解决了什么。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">C. 添加学习型排序\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">使用相同的内存和带宽预算，以便比较公平。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">D. 测量命中率和污染\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">跟踪有用的预取字节与浪费的字节和驱逐。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">E. 测量帧时间分布\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">平均 FPS 不够；检查尾部延迟和卡顿频率。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">F. 压力测试行为变化\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">快速转身、传送、多人游戏不可预测性和程序化场景。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">G. 比较成本\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">预测器 CPU\u002FGPU 时间 + 内存开销必须低于它消除的停顿。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-31\">这个假设的局限\u003C\u002Fh2>\n\u003Cp>预测性内存无法使过小的内存池无限大。它无法创造带宽。它无法通过猜测下一个纹理来修复着色器编译卡顿。当玩家的未来有意不可预测，或者引擎已经拥有近乎完美的确定性流式信息时，它的用处就会降低。\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">与通用 AI 架构的轻微联系\u003C\u002Fh2>\n\u003Cp>更深层的模式类似于游戏之外的检索系统：系统不应获取所有内容，也不应等到最后一刻才发现缺少上下文。它需要一个触发器、一个候选集、一个排序策略和一个有效性边界。游戏只是施加了更严格的延迟预算。\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">什么是 RAG？其工作原理的最简单解释\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">对检索、上下文以及为什么系统获取信息而不是一次性将所有内容放入模型的通用解释。\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">阅读 stajic.de 上的 RAG 解释 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-36\">主要来源\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">基于深度学习的CPU-GPU统一虚拟内存数据预取\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">研究报告了在11个内存密集型基准测试中学习型UVM页面预取的结果。\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">AAAI 2026 — 异步KV缓存预取\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">2026年发表的证据，支持主动利用空闲内存带宽和L2缓存预取。\u003C\u002Fp>\u003C\u002Fa>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">预测性游戏内存\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">AI预取已经是游戏引擎的标准功能了吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">在此提出的特定Figure Rocks架构中并非如此。游戏已经使用了多种形式的预取和预测性流式加载；该假设是将确定性游戏状态与学习型GPU内存预测相结合。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">这会提高RAM或VRAM的速度吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不会。它旨在改善数据移动的时机和内容，使处理器等待更少。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">它能消除卡顿吗？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不能。它只能解决在预测足够准确时由数据可用性\u002F驻留引起的停顿。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">为什么游戏可能是好的预测目标？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">因为引擎暴露了关于摄像机、关卡拓扑、资产、动画和可能可见性的结构化信息，而通用应用程序不具备这些。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">哪个指标最重要？\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">不是单一指标。有用的评估需要页面\u002F资源命中率、带宽开销、缓存污染和帧时间尾部延迟。\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">预测性内存术语\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"prefetch\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">预取\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">在明确需求之前移动或使数据可用。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"residency\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">驻留\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">将资源保持在内存中，距离处理器足够近，以便无需昂贵的传输即可使用。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"cache-pollution\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">缓存污染\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">用最终不需要的预取数据替换有用数据。\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"predictive-gaming-memory\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">预测性游戏内存\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Figure Rocks假设，结合确定性引擎状态、学习型预测、预取和可测量的驻留策略。\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>",{"time":538,"blocks":539,"version":1032},1792339300647,[540,546,554,561,568,574,619,628,633,638,661,666,671,707,714,719,724,751,758,763,830,835,840,845,850,858,863,868,900,905,932,937,942,947,952,960,965,975,984,1011],{"id":541,"data":542,"type":544,"tunes":545},"p-0075",{"text":543},"游戏很少能确切知道未来，但它对接下来几帧的了解远多于通用应用程序。它知道摄像机向量、玩家速度、可见单元格、附近传送门、资源依赖关系、动画状态，而且通常知道接下来可能运行哪些着色器或效果。这使得游戏成为预测性内存管理的一个异常有趣的目标。","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"c-0076",{"body":549,"title":550,"variant":551},"\u003Cstrong>AI辅助预取有可能减少一些与内存相关的卡顿，但它不会是一个通用的防卡顿按钮。\u003C\u002Fstrong>研究已经表明，深度学习预取器可以改善CPU-GPU统一内存的页面预测。游戏引擎可以添加那些基准测试没有的东西：明确的世界和玩家状态。Figure Rocks的假设是将两者结合起来。","直接回答","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"c-0077",{"body":557,"title":558,"variant":559},"本文中的术语\u003Cstrong>预测性游戏内存\u003C\u002Fstrong>描述了一种提议的架构：确定性引擎知识 + 学习到的访问预测 + 预取 + 驻留策略。它不是NVIDIA、Microsoft、Epic或AMD的产品名称。","Figure Rocks假设，而非已宣布的功能","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc-0078",{"title":564,"maxLevel":565,"minLevel":47},"目录",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-0079",{"text":571,"level":47},"首先将内存卡顿与其他卡顿区分开","header",{},{"id":575,"data":576,"type":617,"tunes":618},"cmp-0080",{"rows":577,"title":608,"layout":609,"columns":610},[578,584,590,596,602],{"id":579,"label":580,"values":581},"asset","快速内存中缺少资源",{"other":582,"memory":583},"存储或解压缩也可能涉及","可能",{"id":585,"label":586,"values":587},"shader","着色器未就绪",{"other":588,"memory":589},"编译\u002F管线创建可能是真正原因","可能涉及缓存",{"id":591,"label":592,"values":593},"cpu","CPU模拟峰值",{"other":594,"memory":595},"游戏逻辑或绘制调用工作可能占主导","缓存\u002F内存行为可能有贡献",{"id":597,"label":598,"values":599},"network","在线卡顿",{"other":600,"memory":601},"服务器或数据包时序可能占主导","通常不是主要原因",{"id":603,"label":604,"values":605},"vram","驱逐\u002F驻留压力",{"other":606,"memory":607},"仅从分配计量器可能误诊","直接相关","看起来相似的卡顿来源","table",[611,614],{"id":612,"label":613},"memory","内存\u002F数据路径",{"id":615,"label":616},"other","其他路径","comparison",{},{"id":620,"data":621,"type":626,"tunes":627},"ref-0081",{"url":622,"title":623,"excerpt":624,"ctaLabel":625},"https:\u002F\u002Ffigure.rocks\u002Fzh\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it","为什么PC游戏在编译着色器时会卡顿——以及高级着色器交付如何改变它","着色器卡顿是与内存页面预测不同的问题，不应被归为同一种诊断。","将着色器卡顿与内存卡顿区分开","referralArticle",{},{"id":629,"data":630,"type":572,"tunes":632},"h-0082",{"text":631,"level":47},"预取已经存在；新问题是如何预测",{},{"id":634,"data":635,"type":544,"tunes":637},"p-0083",{"text":636},"传统预取试图在处理器明确请求数据之前移动数据。硬件预取器利用访问模式。引擎在玩家到达世界单元格之前流式传输它们。操作系统和运行时保持可能需要的页面热。困难的部分是错误预测：预取太少仍然会卡顿；预取太多会浪费带宽并驱逐有用数据。",{},{"id":639,"data":640,"type":659,"tunes":660},"flow-0084",{"steps":641,"title":657,"orientation":658},[642,645,648,651,654],{"label":643,"description":644},"观察","收集最近的访问、世界状态和工作负载上下文。",{"label":646,"description":647},"预测","估计接下来需要哪些页面\u002F资源\u002F着色器。",{"label":649,"description":650},"移动","在需求之前传输或使这些资源驻留。",{"label":652,"description":653},"使用","如果预测正确，GPU无需等待即可消耗资源。",{"label":655,"description":656},"纠正","驱逐错误预测并更新模型\u002F策略。","基本的预取权衡","auto","processFlow",{},{"id":662,"data":663,"type":572,"tunes":665},"h-0085",{"text":664,"level":47},"已有研究证据表明学习型GPU预取可行",{},{"id":667,"data":668,"type":544,"tunes":670},"p-0086",{"text":669},"一项关于基于深度学习的CPU-GPU统一虚拟内存预取的已发表研究使用Transformer建模全局内存访问上下文，然后推导出更轻量的预测器。在11个内存密集型基准测试中，论文报告相比对比框架性能提升10.89%，设备内存页面命中率为89.02%对76.10%，CPU-GPU互连流量降低11.05%。",{},{"id":672,"data":673,"type":609,"tunes":706},"t-0087",{"content":674,"stretched":705,"withHeadings":15},[675,680,685,690,695,700],[676,677,678,679],"研究结果","报告值","支持什么","不证明什么",[681,682,683,684],"学习型UVM性能","+10.89%","学习型访问预测可以超越先前的UVM预取基线","商业游戏获得10.89% FPS提升",[686,687,688,689],"UVM页面命中率","89.02% vs 76.10%","预测可以实质性地改变请求数据所在位置","每个游戏纹理\u002F页面都同样可预测",[691,692,693,694],"CPU–GPU互连流量","−11.05%","更好的放置可以减少反应性移动","游戏中PCIe流量总是下降",[696,697,698,699],"AAAI注意力内核效率","2.15×","预取可以将昂贵的内存访问隐藏在有用的计算后面","游戏内核获得2.15×",[701,702,703,704],"AAAI端到端吞吐量","高达1.97×","延迟隐藏可以传播到单个内核之外","1.97×游戏帧率倍增器",false,{},{"id":708,"data":709,"type":552,"tunes":713},"c-0088",{"body":710,"title":711,"variant":712},"这些结果来自论文的基准测试集，\u003Cstrong>而非商业游戏引擎\u003C\u002Fstrong>。它们证明学习型内存访问预测可以在GPU UVM工作负载中起作用。它们不证明游戏中10.89%的FPS提升或特定的卡顿减少。","不要将这些数字复制到游戏上","warning",{},{"id":715,"data":716,"type":544,"tunes":718},"v3-two-evidence-p",{"text":717},"学习型UVM研究有用，因为它从内存行为预测页面访问可能是什么。2026年AAAI KV缓存工作有用的原因不同：它表明调度器可以利用原本空闲的带宽窗口来决定何时在需求之前将数据移入更快的缓存。它们共同支持Figure Rocks假设中的两个独立机制——预测和时机——尽管两篇论文都不是游戏基准测试。",{},{"id":720,"data":721,"type":572,"tunes":723},"h-0089",{"text":722,"level":47},"游戏拥有通用预取器没有的额外信息",{},{"id":725,"data":726,"type":659,"tunes":750},"flow-0090",{"steps":727,"title":749,"orientation":658},[728,731,734,737,740,743,746],{"label":729,"description":730},"1. 读取确定性状态","摄像机方向、位置、速度、关卡图、可见性、动画、任务或遭遇状态。",{"label":732,"description":733},"2. 读取访问历史","最近使用了哪些纹理页面、网格、着色器、几何集群和模型数据。",{"label":735,"description":736},"3. 预测近期工作集","根据成本估计未来几帧或几秒可能需要的资源。",{"label":738,"description":739},"4. 分配置信度和成本","95%可能的4 MB资源与20%可能的2 GB区域是不同的决策。",{"label":741,"description":742},"5. 在带宽预算内预取","不要让预测本身饱和当前渲染所需的路径。",{"label":744,"description":745},"6. 测量未命中和污染","跟踪避免的卡顿、错误预测和被驱逐的有用数据。",{"label":747,"description":748},"7. 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Stajic",[1081,1490],{"lang":8,"title":1082,"content":1083,"contentJson":1084,"excerpt":1489},"What If a Game Could Predict Its Next Memory Access? AI Prefetching and the Future of Stutter","{\"time\":1792339102894,\"blocks\":[{\"id\":\"p-0075\",\"type\":\"paragraph\",\"data\":{\"text\":\"A game rarely knows the exact future, but it knows much more about the next few frames than a general-purpose application does. It knows the camera vector, player velocity, visible cells, nearby portals, asset dependencies, animation state and often which shaders or effects are likely to run next. That makes gaming an unusually interesting target for predictive memory management.\"},\"tunes\":{}},{\"id\":\"c-0076\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>AI-assisted prefetching could plausibly reduce some memory-related stalls, but it would not be a universal anti-stutter button.\u003C\u002Fstrong> Research already shows deep-learning prefetchers improving CPU-GPU unified-memory page prediction. A game engine could add something those benchmarks do not have: explicit world and player state. The Figure Rocks hypothesis is to combine both.\"},\"tunes\":{}},{\"id\":\"c-0077\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Figure Rocks hypothesis, not an announced feature\",\"body\":\"The term \u003Cstrong>Predictive Gaming Memory\u003C\u002Fstrong> in this article describes a proposed architecture: deterministic engine knowledge + learned access prediction + prefetch + residency policy. It is not an NVIDIA, Microsoft, Epic or AMD product name.\"},\"tunes\":{}},{\"id\":\"toc-0078\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-0079\",\"type\":\"header\",\"data\":{\"text\":\"First separate memory stalls from other stutter\",\"level\":2},\"tunes\":{}},{\"id\":\"cmp-0080\",\"type\":\"comparison\",\"data\":{\"title\":\"Stutter sources that can look similar\",\"layout\":\"table\",\"columns\":[{\"id\":\"memory\",\"label\":\"Memory\u002Fdata path\"},{\"id\":\"other\",\"label\":\"Other path\"}],\"rows\":[{\"id\":\"asset\",\"label\":\"Asset missing from fast memory\",\"values\":{\"memory\":\"Possible\",\"other\":\"Storage or decompression can also be involved\"}},{\"id\":\"shader\",\"label\":\"Shader not ready\",\"values\":{\"memory\":\"May involve caches\",\"other\":\"Compilation\u002Fpipeline creation can be the real cause\"}},{\"id\":\"cpu\",\"label\":\"CPU simulation spike\",\"values\":{\"memory\":\"Cache\u002Fmemory behavior can contribute\",\"other\":\"Game logic or draw-call work may dominate\"}},{\"id\":\"network\",\"label\":\"Online hitch\",\"values\":{\"memory\":\"Usually not the primary cause\",\"other\":\"Server or packet timing can dominate\"}},{\"id\":\"vram\",\"label\":\"Eviction\u002Fresidency pressure\",\"values\":{\"memory\":\"Directly relevant\",\"other\":\"Can be misdiagnosed from allocation meters alone\"}}]},\"tunes\":{}},{\"id\":\"ref-0081\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it\",\"title\":\"Why PC Games Stutter When Compiling Shaders — and How Advanced Shader Delivery Changes It\",\"excerpt\":\"Shader stutter is a different problem from memory-page prediction and should not be collapsed into one diagnosis.\",\"ctaLabel\":\"Separate shader stutter from memory stalls\"},\"tunes\":{}},{\"id\":\"h-0082\",\"type\":\"header\",\"data\":{\"text\":\"Prefetching already exists; the new question is how it predicts\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0083\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional prefetching tries to move data before a processor explicitly asks for it. Hardware prefetchers exploit access patterns. Engines stream world cells before the player reaches them. Operating systems and runtimes keep likely-needed pages hot. The difficult part is false prediction: prefetch too little and you still stall; prefetch too much and you waste bandwidth and evict useful data.\"},\"tunes\":{}},{\"id\":\"flow-0084\",\"type\":\"processFlow\",\"data\":{\"title\":\"The basic prefetch trade-off\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Observe\",\"description\":\"Collect recent accesses, world state and workload context.\"},{\"label\":\"Predict\",\"description\":\"Estimate which pages\u002Fassets\u002Fshaders will be needed next.\"},{\"label\":\"Move\",\"description\":\"Transfer or make those resources resident before demand.\"},{\"label\":\"Use\",\"description\":\"The GPU consumes the resource without waiting if prediction was correct.\"},{\"label\":\"Correct\",\"description\":\"Evict wrong predictions and update the model\u002Fpolicy.\"}]},\"tunes\":{}},{\"id\":\"h-0085\",\"type\":\"header\",\"data\":{\"text\":\"There is already research evidence for learned GPU prefetching\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0086\",\"type\":\"paragraph\",\"data\":{\"text\":\"A published study on deep-learning-based prefetching for CPU-GPU Unified Virtual Memory used a Transformer to model global memory-access context and then derived a lighter predictor. Across 11 memory-intensive benchmarks, the paper reported 10.89% performance improvement over the compared framework, a device-memory page hit rate of 89.02% versus 76.10%, and 11.05% lower CPU-GPU interconnect traffic.\"},\"tunes\":{}},{\"id\":\"t-0087\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Research result\",\"Reported value\",\"What it supports\",\"What it does NOT prove\"],[\"Learned UVM performance\",\"+10.89%\",\"Learned access prediction can beat a prior UVM prefetch baseline\",\"A commercial game gains 10.89% FPS\"],[\"UVM page hit rate\",\"89.02% vs 76.10%\",\"Prediction can materially change where requested data is found\",\"Every game texture\u002Fpage is equally predictable\"],[\"CPU–GPU interconnect traffic\",\"−11.05%\",\"Better placement can reduce reactive movement\",\"PCIe traffic always falls in games\"],[\"AAAI attention-kernel efficiency\",\"2.15×\",\"Prefetch can hide expensive memory access behind useful compute\",\"Gaming kernels receive 2.15×\"],[\"AAAI end-to-end throughput\",\"Up to 1.97×\",\"Latency hiding can propagate beyond one kernel\",\"A 1.97× game frame-rate multiplier\"]]},\"tunes\":{}},{\"id\":\"c-0088\",\"type\":\"callout\",\"data\":{\"variant\":\"warning\",\"title\":\"Do not copy these numbers onto games\",\"body\":\"Those results come from the paper's benchmark set, \u003Cstrong>not from commercial game engines\u003C\u002Fstrong>. They prove that learned memory-access prediction can work in GPU UVM workloads. They do not prove a 10.89% FPS gain or a specific stutter reduction in a game.\"},\"tunes\":{}},{\"id\":\"v3-two-evidence-p\",\"type\":\"paragraph\",\"data\":{\"text\":\"The learned-UVM study is useful because it predicts what page access is likely from memory behavior. The 2026 AAAI KV-cache work is useful for a different reason: it shows that a scheduler can exploit otherwise idle bandwidth windows to decide when to move data into a faster cache before demand. Together they support two separate mechanisms in the Figure Rocks hypothesis — prediction and timing — while neither paper is a gaming benchmark.\"},\"tunes\":{}},{\"id\":\"h-0089\",\"type\":\"header\",\"data\":{\"text\":\"Games have extra information a generic prefetcher does not\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0090\",\"type\":\"processFlow\",\"data\":{\"title\":\"The Predictive Gaming Memory Loop\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Read deterministic state\",\"description\":\"Camera direction, position, velocity, level graph, visibility, animation, quest or encounter state.\"},{\"label\":\"2. Read access history\",\"description\":\"Which texture pages, meshes, shaders, geometry clusters and model data were recently used.\"},{\"label\":\"3. Predict the near-future working set\",\"description\":\"Estimate likely-needed resources several frames or seconds ahead depending on cost.\"},{\"label\":\"4. Assign confidence and cost\",\"description\":\"A 95% likely 4 MB asset is a different decision from a 20% likely 2 GB region.\"},{\"label\":\"5. Prefetch within a bandwidth budget\",\"description\":\"Do not let prediction itself saturate the path needed by current rendering.\"},{\"label\":\"6. Measure misses and pollution\",\"description\":\"Track stalls avoided, wrong predictions and useful data evicted.\"},{\"label\":\"7. Adapt\",\"description\":\"Update the predictor or engine policy for the actual scene and player behavior.\"}]},\"tunes\":{}},{\"id\":\"c-0091\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The interesting hybrid\",\"body\":\"A game does not have to choose between hand-authored streaming and AI. \u003Cstrong>The strongest design may be hybrid:\u003C\u002Fstrong> deterministic rules guarantee critical resources, while a learned predictor ranks optional or near-future work, chooses prefetch depth and spends only safe bandwidth headroom. If confidence is low, the system falls back to authored rules.\"},\"tunes\":{}},{\"id\":\"v3-known-future\",\"type\":\"header\",\"data\":{\"text\":\"The engine can combine known future with predicted future\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-known-pred\",\"type\":\"comparison\",\"data\":{\"title\":\"Deterministic knowledge vs learned prediction\",\"layout\":\"table\",\"columns\":[{\"id\":\"known\",\"label\":\"Engine already knows\"},{\"id\":\"learned\",\"label\":\"Predictor can estimate\"}],\"rows\":[{\"id\":\"camera\",\"label\":\"Camera \u002F portal state\",\"values\":{\"known\":\"Current view, frustum, room graph, visible portal\",\"learned\":\"Which direction the player is likely to turn next\"}},{\"id\":\"movement\",\"label\":\"Player movement\",\"values\":{\"known\":\"Velocity, input, navigation constraints\",\"learned\":\"Likely traversal path several moments ahead\"}},{\"id\":\"assets\",\"label\":\"Asset dependencies\",\"values\":{\"known\":\"Dependencies for a known level cell or object\",\"learned\":\"Which optional neighboring assets will become hot\"}},{\"id\":\"lod\",\"label\":\"LOD state\",\"values\":{\"known\":\"Current distance and thresholds\",\"learned\":\"Which LOD transitions are likely soon\"}},{\"id\":\"shaders\",\"label\":\"Material\u002Fshader graph\",\"values\":{\"known\":\"Shaders required by known visible content\",\"learned\":\"Which variants are likely to be requested next\"}},{\"id\":\"strength\",\"label\":\"Core strength\",\"values\":{\"known\":\"Knows hard level rules, dependencies and guaranteed transitions\",\"learned\":\"Ranks ambiguous future access from behavior and context\"}},{\"id\":\"risk\",\"label\":\"Failure mode\",\"values\":{\"known\":\"Can be conservative and load broad sets\",\"learned\":\"Can be wrong and pollute cache or memory\"}},{\"id\":\"best\",\"label\":\"Best use\",\"values\":{\"known\":\"Critical assets and guaranteed transitions\",\"learned\":\"Optional prioritization inside a bounded bandwidth budget\"}},{\"id\":\"safety\",\"label\":\"Safety policy\",\"values\":{\"known\":\"Easy to reason about and test\",\"learned\":\"Needs confidence thresholds, telemetry and deterministic fallback\"}}]},\"tunes\":{}},{\"id\":\"h-0092\",\"type\":\"header\",\"data\":{\"text\":\"A simple example: running toward a door\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0093\",\"type\":\"paragraph\",\"data\":{\"text\":\"Imagine a player sprinting down a corridor toward a door that opens into a dense market square. A conventional streaming system already knows the next zone and can begin loading it. Predictive memory adds a second layer: based on camera direction, movement, prior player choices and current mission state, it can rank which subset of textures, NPC animation data, shaders and geometry is most likely to be needed first.\"},\"tunes\":{}},{\"id\":\"h-0095\",\"type\":\"header\",\"data\":{\"text\":\"Why this could matter more as neural rendering grows\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0096\",\"type\":\"paragraph\",\"data\":{\"text\":\"Neural graphics adds new data classes: model weights, latent or compressed representations, history buffers and intermediate tensors. If those resources compete with textures and geometry, residency becomes more complicated. Predictive scheduling could decide not only which asset to move, but which representation to keep: raw data, a compressed neural representation, or a reconstructable form.\"},\"tunes\":{}},{\"id\":\"ref-0097\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\",\"title\":\"DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes\",\"excerpt\":\"Neural rendering changes what data a frame needs and when it needs it.\",\"ctaLabel\":\"Read the neural rendering analysis\"},\"tunes\":{}},{\"id\":\"h-0098\",\"type\":\"header\",\"data\":{\"text\":\"The failure modes are as important as the opportunity\",\"level\":2},\"tunes\":{}},{\"id\":\"v3-bandwidth-budget-p\",\"type\":\"paragraph\",\"data\":{\"text\":\"Prefetch is not free. A wrong prediction can fetch resources the player never sees, evict useful pages and compete with frame-critical traffic. The correct goal is therefore not maximum prefetch; it is maximum useful hit rate inside a bounded bandwidth and residency budget.\"},\"tunes\":{}},{\"id\":\"t-0099\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Failure mode\",\"What happens\",\"Mitigation idea\"],[\"Bad prediction\",\"Wrong data is prefetched\",\"Confidence thresholds; reserve budget for deterministic demand\"],[\"Cache pollution\",\"Useful current data is evicted\",\"Separate predicted and protected residency classes\"],[\"Bandwidth contention\",\"Prefetch steals bandwidth from current frame\",\"Schedule only in headroom windows; enforce per-frame budget\"],[\"Behavior shift\",\"Player turns around or fast-travels\",\"Cheap cancellation and rapid fallback\"],[\"Model overhead\",\"Predictor costs more than it saves\",\"Small models, offline training, or rule-derived lightweight predictors\"],[\"Non-memory stutter\",\"Problem remains\",\"Keep diagnosis separate: shader, CPU, network and storage paths need their own tests\"]]},\"tunes\":{}},{\"id\":\"h-0100\",\"type\":\"header\",\"data\":{\"text\":\"How you would test it in a real engine\",\"level\":2},\"tunes\":{}},{\"id\":\"flow-0101\",\"type\":\"processFlow\",\"data\":{\"title\":\"Predictive Memory Evaluation\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"A. Establish baseline\",\"description\":\"Capture frame-time spikes, page faults\u002Fmigrations, residency misses and data-transfer volume without prediction.\"},{\"label\":\"B. Add deterministic prefetch only\",\"description\":\"Measure what conventional engine knowledge already solves.\"},{\"label\":\"C. Add learned ranking\",\"description\":\"Use the same memory and bandwidth budget so the comparison is fair.\"},{\"label\":\"D. Measure hit rate and pollution\",\"description\":\"Track useful prefetched bytes versus wasted bytes and evictions.\"},{\"label\":\"E. Measure frame-time distribution\",\"description\":\"Average FPS is not enough; inspect tail latency and hitch frequency.\"},{\"label\":\"F. Stress behavior changes\",\"description\":\"Fast turns, teleport, multiplayer unpredictability and procedural scenes.\"},{\"label\":\"G. Compare cost\",\"description\":\"Predictor CPU\u002FGPU time + memory overhead must be lower than the stalls it removes.\"}]},\"tunes\":{}},{\"id\":\"h-0102\",\"type\":\"header\",\"data\":{\"text\":\"Where this hypothesis stops\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0103\",\"type\":\"paragraph\",\"data\":{\"text\":\"Predictive memory cannot make a too-small memory pool infinitely large. It cannot create bandwidth. It cannot fix a shader compilation hitch by guessing the next texture. And it becomes less useful when the player's future is intentionally unpredictable or when the engine already has near-perfect deterministic streaming information.\"},\"tunes\":{}},{\"id\":\"h-0104\",\"type\":\"header\",\"data\":{\"text\":\"A light connection to general AI architecture\",\"level\":2},\"tunes\":{}},{\"id\":\"p-0105\",\"type\":\"paragraph\",\"data\":{\"text\":\"The deeper pattern is similar to retrieval systems outside gaming: the system should not fetch everything, and it should not wait until the final moment to discover that context is missing. It needs a trigger, a candidate set, a ranking policy and a validity boundary. Gaming simply imposes a much tighter latency budget.\"},\"tunes\":{}},{\"id\":\"ref-0106\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Fstajic.de\u002Fblog\u002Fwhat-is-rag-the-simplest-explanation-of-how-it-works\",\"title\":\"What Is RAG? The Simplest Explanation of How It Works\",\"excerpt\":\"A general explanation of retrieval, context and why systems fetch information instead of placing everything in the model at once.\",\"ctaLabel\":\"Read the RAG explainer on stajic.de\"},\"tunes\":{}},{\"id\":\"h-0107\",\"type\":\"header\",\"data\":{\"text\":\"Primary source\",\"level\":2},\"tunes\":{}},{\"id\":\"src-0108\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0743731522002490\",\"meta\":{\"title\":\"Deep learning based data prefetching in CPU-GPU Unified Virtual Memory\",\"description\":\"Research paper reporting learned UVM page-prefetching results across 11 memory-intensive benchmarks.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"v3-src-aaai2\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F39224\",\"meta\":{\"title\":\"AAAI 2026 — Asynchronous KV Cache Prefetching\",\"description\":\"Published 2026 evidence for proactively using idle memory bandwidth and L2 cache prefetch.\",\"image\":{\"url\":\"\"}}},\"tunes\":{}},{\"id\":\"faq-0109\",\"type\":\"faq\",\"data\":{\"title\":\"Predictive gaming memory\",\"items\":[{\"id\":\"faq1\",\"question\":\"Is AI prefetching already a standard game-engine feature?\",\"answer\":\"Not in the specific Figure Rocks architecture proposed here. Games already use many forms of prefetching and predictive streaming; the hypothesis is to combine deterministic game state with learned GPU-memory prediction.\"},{\"id\":\"faq2\",\"question\":\"Would this increase RAM or VRAM speed?\",\"answer\":\"No. It aims to improve when and what data is moved so the processor waits less often.\"},{\"id\":\"faq3\",\"question\":\"Could it eliminate stutter?\",\"answer\":\"No. It can only address stalls caused by data availability\u002Fresidency when prediction is accurate enough.\"},{\"id\":\"faq4\",\"question\":\"Why might games be a good prediction target?\",\"answer\":\"Because engines expose structured information about camera, level topology, assets, animation and likely visibility that a generic application does not.\"},{\"id\":\"faq5\",\"question\":\"What metric matters most?\",\"answer\":\"Not one metric. Useful evaluation needs page\u002Fresource hit rate, bandwidth overhead, cache pollution and frame-time tail latency.\"}]},\"tunes\":{}},{\"id\":\"gloss-0110\",\"type\":\"glossary\",\"data\":{\"title\":\"Predictive memory terms\",\"entries\":[{\"term\":\"Prefetch\",\"definition\":\"Moving or making data available before explicit demand.\",\"anchor\":\"prefetch\"},{\"term\":\"Residency\",\"definition\":\"Keeping the resource in memory close enough to the processor that it can be used without an expensive transfer.\",\"anchor\":\"residency\"},{\"term\":\"Cache pollution\",\"definition\":\"Replacing useful data with prefetched data that turns out not to be needed.\",\"anchor\":\"cache-pollution\"},{\"term\":\"Predictive Gaming Memory\",\"definition\":\"Figure Rocks hypothesis combining deterministic engine state, learned prediction, prefetch and measured residency policy.\",\"anchor\":\"predictive-gaming-memory\"}]},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1085,"blocks":1086,"version":1488},1792339102894,[1087,1091,1096,1101,1105,1109,1144,1151,1155,1159,1179,1183,1187,1217,1222,1226,1230,1256,1261,1265,1320,1324,1328,1332,1336,1343,1347,1351,1382,1386,1412,1416,1420,1424,1428,1434,1438,1445,1452,1472],{"id":541,"data":1088,"type":544,"tunes":1090},{"text":1089},"A game rarely knows the exact future, but it knows much more about the next few frames than a general-purpose application does. It knows the camera vector, player velocity, visible cells, nearby portals, asset dependencies, animation state and often which shaders or effects are likely to run next. That makes gaming an unusually interesting target for predictive memory management.",{},{"id":547,"data":1092,"type":552,"tunes":1095},{"body":1093,"title":1094,"variant":551},"\u003Cstrong>AI-assisted prefetching could plausibly reduce some memory-related stalls, but it would not be a universal anti-stutter button.\u003C\u002Fstrong> Research already shows deep-learning prefetchers improving CPU-GPU unified-memory page prediction. A game engine could add something those benchmarks do not have: explicit world and player state. The Figure Rocks hypothesis is to combine both.","Direct answer",{},{"id":555,"data":1097,"type":552,"tunes":1100},{"body":1098,"title":1099,"variant":559},"The term \u003Cstrong>Predictive Gaming Memory\u003C\u002Fstrong> in this article describes a proposed architecture: deterministic engine knowledge + learned access prediction + prefetch + residency policy. It is not an NVIDIA, Microsoft, Epic or AMD product name.","Figure Rocks hypothesis, not an announced feature",{},{"id":562,"data":1102,"type":566,"tunes":1104},{"title":1103,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1106,"type":572,"tunes":1108},{"text":1107,"level":47},"First separate memory stalls from other stutter",{},{"id":575,"data":1110,"type":617,"tunes":1143},{"rows":1111,"title":1137,"layout":609,"columns":1138},[1112,1117,1122,1127,1132],{"id":579,"label":1113,"values":1114},"Asset missing from fast memory",{"other":1115,"memory":1116},"Storage or decompression can also be involved","Possible",{"id":585,"label":1118,"values":1119},"Shader not ready",{"other":1120,"memory":1121},"Compilation\u002Fpipeline creation can be the real cause","May involve caches",{"id":591,"label":1123,"values":1124},"CPU simulation spike",{"other":1125,"memory":1126},"Game logic or draw-call work may dominate","Cache\u002Fmemory behavior can contribute",{"id":597,"label":1128,"values":1129},"Online hitch",{"other":1130,"memory":1131},"Server or packet timing can dominate","Usually not the primary cause",{"id":603,"label":1133,"values":1134},"Eviction\u002Fresidency pressure",{"other":1135,"memory":1136},"Can be misdiagnosed from allocation meters alone","Directly relevant","Stutter sources that can look similar",[1139,1141],{"id":612,"label":1140},"Memory\u002Fdata path",{"id":615,"label":1142},"Other path",{},{"id":620,"data":1145,"type":626,"tunes":1150},{"url":1146,"title":1147,"excerpt":1148,"ctaLabel":1149},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fwhy-pc-games-stutter-when-compiling-shaders-and-how-advanced-shader-delivery-changes-it","Why PC Games Stutter When Compiling Shaders — and How Advanced Shader Delivery Changes It","Shader stutter is a different problem from memory-page prediction and should not be collapsed into one diagnosis.","Separate shader stutter from memory stalls",{},{"id":629,"data":1152,"type":572,"tunes":1154},{"text":1153,"level":47},"Prefetching already exists; the new question is how it predicts",{},{"id":634,"data":1156,"type":544,"tunes":1158},{"text":1157},"Traditional prefetching tries to move data before a processor explicitly asks for it. Hardware prefetchers exploit access patterns. Engines stream world cells before the player reaches them. Operating systems and runtimes keep likely-needed pages hot. The difficult part is false prediction: prefetch too little and you still stall; prefetch too much and you waste bandwidth and evict useful data.",{},{"id":639,"data":1160,"type":659,"tunes":1178},{"steps":1161,"title":1177,"orientation":658},[1162,1165,1168,1171,1174],{"label":1163,"description":1164},"Observe","Collect recent accesses, world state and workload context.",{"label":1166,"description":1167},"Predict","Estimate which pages\u002Fassets\u002Fshaders will be needed next.",{"label":1169,"description":1170},"Move","Transfer or make those resources resident before demand.",{"label":1172,"description":1173},"Use","The GPU consumes the resource without waiting if prediction was correct.",{"label":1175,"description":1176},"Correct","Evict wrong predictions and update the model\u002Fpolicy.","The basic prefetch trade-off",{},{"id":662,"data":1180,"type":572,"tunes":1182},{"text":1181,"level":47},"There is already research evidence for learned GPU prefetching",{},{"id":667,"data":1184,"type":544,"tunes":1186},{"text":1185},"A published study on deep-learning-based prefetching for CPU-GPU Unified Virtual Memory used a Transformer to model global memory-access context and then derived a lighter predictor. Across 11 memory-intensive benchmarks, the paper reported 10.89% performance improvement over the compared framework, a device-memory page hit rate of 89.02% versus 76.10%, and 11.05% lower CPU-GPU interconnect traffic.",{},{"id":672,"data":1188,"type":609,"tunes":1216},{"content":1189,"stretched":705,"withHeadings":15},[1190,1195,1199,1203,1207,1211],[1191,1192,1193,1194],"Research result","Reported value","What it supports","What it does NOT prove",[1196,682,1197,1198],"Learned UVM performance","Learned access prediction can beat a prior UVM prefetch baseline","A commercial game gains 10.89% FPS",[1200,687,1201,1202],"UVM page hit rate","Prediction can materially change where requested data is found","Every game texture\u002Fpage is equally predictable",[1204,692,1205,1206],"CPU–GPU interconnect traffic","Better placement can reduce reactive movement","PCIe traffic always falls in games",[1208,697,1209,1210],"AAAI attention-kernel efficiency","Prefetch can hide expensive memory access behind useful compute","Gaming kernels receive 2.15×",[1212,1213,1214,1215],"AAAI end-to-end throughput","Up to 1.97×","Latency hiding can propagate beyond one kernel","A 1.97× game frame-rate multiplier",{},{"id":708,"data":1218,"type":552,"tunes":1221},{"body":1219,"title":1220,"variant":712},"Those results come from the paper's benchmark set, \u003Cstrong>not from commercial game engines\u003C\u002Fstrong>. They prove that learned memory-access prediction can work in GPU UVM workloads. They do not prove a 10.89% FPS gain or a specific stutter reduction in a game.","Do not copy these numbers onto games",{},{"id":715,"data":1223,"type":544,"tunes":1225},{"text":1224},"The learned-UVM study is useful because it predicts what page access is likely from memory behavior. The 2026 AAAI KV-cache work is useful for a different reason: it shows that a scheduler can exploit otherwise idle bandwidth windows to decide when to move data into a faster cache before demand. Together they support two separate mechanisms in the Figure Rocks hypothesis — prediction and timing — while neither paper is a gaming benchmark.",{},{"id":720,"data":1227,"type":572,"tunes":1229},{"text":1228,"level":47},"Games have extra information a generic prefetcher does not",{},{"id":725,"data":1231,"type":659,"tunes":1255},{"steps":1232,"title":1254,"orientation":658},[1233,1236,1239,1242,1245,1248,1251],{"label":1234,"description":1235},"1. Read deterministic state","Camera direction, position, velocity, level graph, visibility, animation, quest or encounter state.",{"label":1237,"description":1238},"2. Read access history","Which texture pages, meshes, shaders, geometry clusters and model data were recently used.",{"label":1240,"description":1241},"3. Predict the near-future working set","Estimate likely-needed resources several frames or seconds ahead depending on cost.",{"label":1243,"description":1244},"4. Assign confidence and cost","A 95% likely 4 MB asset is a different decision from a 20% likely 2 GB region.",{"label":1246,"description":1247},"5. Prefetch within a bandwidth budget","Do not let prediction itself saturate the path needed by current rendering.",{"label":1249,"description":1250},"6. Measure misses and pollution","Track stalls avoided, wrong predictions and useful data evicted.",{"label":1252,"description":1253},"7. Adapt","Update the predictor or engine policy for the actual scene and player behavior.","The Predictive Gaming Memory Loop",{},{"id":752,"data":1257,"type":552,"tunes":1260},{"body":1258,"title":1259,"variant":756},"A game does not have to choose between hand-authored streaming and AI. \u003Cstrong>The strongest design may be hybrid:\u003C\u002Fstrong> deterministic rules guarantee critical resources, while a learned predictor ranks optional or near-future work, chooses prefetch depth and spends only safe bandwidth headroom. If confidence is low, the system falls back to authored rules.","The interesting hybrid",{},{"id":759,"data":1262,"type":572,"tunes":1264},{"text":1263,"level":47},"The engine can combine known future with predicted future",{},{"id":764,"data":1266,"type":617,"tunes":1319},{"rows":1267,"title":1313,"layout":609,"columns":1314},[1268,1273,1278,1283,1288,1293,1298,1303,1308],{"id":768,"label":1269,"values":1270},"Camera \u002F portal state",{"known":1271,"learned":1272},"Current view, frustum, room graph, visible portal","Which direction the player is likely to turn next",{"id":774,"label":1274,"values":1275},"Player movement",{"known":1276,"learned":1277},"Velocity, input, navigation constraints","Likely traversal path several moments ahead",{"id":780,"label":1279,"values":1280},"Asset dependencies",{"known":1281,"learned":1282},"Dependencies for a known level cell or object","Which optional neighboring assets will become hot",{"id":786,"label":1284,"values":1285},"LOD state",{"known":1286,"learned":1287},"Current distance and thresholds","Which LOD transitions are likely soon",{"id":792,"label":1289,"values":1290},"Material\u002Fshader graph",{"known":1291,"learned":1292},"Shaders required by known visible content","Which variants are likely to be requested next",{"id":798,"label":1294,"values":1295},"Core strength",{"known":1296,"learned":1297},"Knows hard level rules, dependencies and guaranteed transitions","Ranks ambiguous future access from behavior and context",{"id":804,"label":1299,"values":1300},"Failure mode",{"known":1301,"learned":1302},"Can be conservative and load broad sets","Can be wrong and pollute cache or memory",{"id":810,"label":1304,"values":1305},"Best use",{"known":1306,"learned":1307},"Critical assets and guaranteed transitions","Optional prioritization inside a bounded bandwidth budget",{"id":816,"label":1309,"values":1310},"Safety policy",{"known":1311,"learned":1312},"Easy to reason about and test","Needs confidence thresholds, telemetry and deterministic fallback","Deterministic knowledge vs learned prediction",[1315,1317],{"id":824,"label":1316},"Engine already knows",{"id":827,"label":1318},"Predictor can estimate",{},{"id":831,"data":1321,"type":572,"tunes":1323},{"text":1322,"level":47},"A simple example: running toward a door",{},{"id":836,"data":1325,"type":544,"tunes":1327},{"text":1326},"Imagine a player sprinting down a corridor toward a door that opens into a dense market square. A conventional streaming system already knows the next zone and can begin loading it. Predictive memory adds a second layer: based on camera direction, movement, prior player choices and current mission state, it can rank which subset of textures, NPC animation data, shaders and geometry is most likely to be needed first.",{},{"id":841,"data":1329,"type":572,"tunes":1331},{"text":1330,"level":47},"Why this could matter more as neural rendering grows",{},{"id":846,"data":1333,"type":544,"tunes":1335},{"text":1334},"Neural graphics adds new data classes: model weights, latent or compressed representations, history buffers and intermediate tensors. If those resources compete with textures and geometry, residency becomes more complicated. Predictive scheduling could decide not only which asset to move, but which representation to keep: raw data, a compressed neural representation, or a reconstructable form.",{},{"id":851,"data":1337,"type":626,"tunes":1342},{"url":1338,"title":1339,"excerpt":1340,"ctaLabel":1341},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 Is Not Just Upscaling: What Generative Neural Rendering Actually Changes","Neural rendering changes what data a frame needs and when it needs it.","Read the neural rendering analysis",{},{"id":859,"data":1344,"type":572,"tunes":1346},{"text":1345,"level":47},"The failure modes are as important as the opportunity",{},{"id":864,"data":1348,"type":544,"tunes":1350},{"text":1349},"Prefetch is not free. A wrong prediction can fetch resources the player never sees, evict useful pages and compete with frame-critical traffic. The correct goal is therefore not maximum prefetch; it is maximum useful hit rate inside a bounded bandwidth and residency budget.",{},{"id":869,"data":1352,"type":609,"tunes":1381},{"content":1353,"stretched":705,"withHeadings":15},[1354,1357,1361,1365,1369,1373,1377],[1299,1355,1356],"What happens","Mitigation idea",[1358,1359,1360],"Bad prediction","Wrong data is prefetched","Confidence thresholds; reserve budget for deterministic demand",[1362,1363,1364],"Cache pollution","Useful current data is evicted","Separate predicted and protected residency classes",[1366,1367,1368],"Bandwidth contention","Prefetch steals bandwidth from current frame","Schedule only in headroom windows; enforce per-frame budget",[1370,1371,1372],"Behavior shift","Player turns around or fast-travels","Cheap cancellation and rapid fallback",[1374,1375,1376],"Model overhead","Predictor costs more than it saves","Small models, offline training, or rule-derived lightweight predictors",[1378,1379,1380],"Non-memory stutter","Problem remains","Keep diagnosis separate: shader, CPU, network and storage paths need their own tests",{},{"id":901,"data":1383,"type":572,"tunes":1385},{"text":1384,"level":47},"How you would test it in a real engine",{},{"id":906,"data":1387,"type":659,"tunes":1411},{"steps":1388,"title":1410,"orientation":658},[1389,1392,1395,1398,1401,1404,1407],{"label":1390,"description":1391},"A. Establish baseline","Capture frame-time spikes, page faults\u002Fmigrations, residency misses and data-transfer volume without prediction.",{"label":1393,"description":1394},"B. Add deterministic prefetch only","Measure what conventional engine knowledge already solves.",{"label":1396,"description":1397},"C. Add learned ranking","Use the same memory and bandwidth budget so the comparison is fair.",{"label":1399,"description":1400},"D. Measure hit rate and pollution","Track useful prefetched bytes versus wasted bytes and evictions.",{"label":1402,"description":1403},"E. Measure frame-time distribution","Average FPS is not enough; inspect tail latency and hitch frequency.",{"label":1405,"description":1406},"F. Stress behavior changes","Fast turns, teleport, multiplayer unpredictability and procedural scenes.",{"label":1408,"description":1409},"G. Compare cost","Predictor CPU\u002FGPU time + memory overhead must be lower than the stalls it removes.","Predictive Memory Evaluation",{},{"id":933,"data":1413,"type":572,"tunes":1415},{"text":1414,"level":47},"Where this hypothesis stops",{},{"id":938,"data":1417,"type":544,"tunes":1419},{"text":1418},"Predictive memory cannot make a too-small memory pool infinitely large. It cannot create bandwidth. It cannot fix a shader compilation hitch by guessing the next texture. And it becomes less useful when the player's future is intentionally unpredictable or when the engine already has near-perfect deterministic streaming information.",{},{"id":943,"data":1421,"type":572,"tunes":1423},{"text":1422,"level":47},"A light connection to general AI architecture",{},{"id":948,"data":1425,"type":544,"tunes":1427},{"text":1426},"The deeper pattern is similar to retrieval systems outside gaming: the system should not fetch everything, and it should not wait until the final moment to discover that context is missing. It needs a trigger, a candidate set, a ranking policy and a validity boundary. Gaming simply imposes a much tighter latency budget.",{},{"id":953,"data":1429,"type":626,"tunes":1433},{"url":955,"title":1430,"excerpt":1431,"ctaLabel":1432},"What Is RAG? The Simplest Explanation of How It Works","A general explanation of retrieval, context and why systems fetch information instead of placing everything in the model at once.","Read the RAG explainer on stajic.de",{},{"id":961,"data":1435,"type":572,"tunes":1437},{"text":1436,"level":47},"Primary source",{},{"id":966,"data":1439,"type":973,"tunes":1444},{"link":968,"meta":1440},{"image":1441,"title":1442,"description":1443},{"url":13},"Deep learning based data prefetching in CPU-GPU Unified Virtual Memory","Research paper reporting learned UVM page-prefetching results across 11 memory-intensive benchmarks.",{},{"id":976,"data":1446,"type":973,"tunes":1451},{"link":978,"meta":1447},{"image":1448,"title":1449,"description":1450},{"url":13},"AAAI 2026 — Asynchronous KV Cache Prefetching","Published 2026 evidence for proactively using idle memory bandwidth and L2 cache prefetch.",{},{"id":985,"data":1453,"type":1009,"tunes":1471},{"items":1454,"title":1470},[1455,1458,1461,1464,1467],{"id":989,"answer":1456,"question":1457},"Not in the specific Figure Rocks architecture proposed here. Games already use many forms of prefetching and predictive streaming; the hypothesis is to combine deterministic game state with learned GPU-memory prediction.","Is AI prefetching already a standard game-engine feature?",{"id":993,"answer":1459,"question":1460},"No. It aims to improve when and what data is moved so the processor waits less often.","Would this increase RAM or VRAM speed?",{"id":997,"answer":1462,"question":1463},"No. It can only address stalls caused by data availability\u002Fresidency when prediction is accurate enough.","Could it eliminate stutter?",{"id":1001,"answer":1465,"question":1466},"Because engines expose structured information about camera, level topology, assets, animation and likely visibility that a generic application does not.","Why might games be a good prediction target?",{"id":1005,"answer":1468,"question":1469},"Not one metric. Useful evaluation needs page\u002Fresource hit rate, bandwidth overhead, cache pollution and frame-time tail latency.","What metric matters most?","Predictive gaming memory",{},{"id":1012,"data":1473,"type":1030,"tunes":1487},{"title":1474,"entries":1475},"Predictive memory terms",[1476,1479,1482,1484],{"term":1477,"anchor":1018,"definition":1478},"Prefetch","Moving or making data available before explicit demand.",{"term":1480,"anchor":1022,"definition":1481},"Residency","Keeping the resource in memory close enough to the processor that it can be used without an expensive transfer.",{"term":1362,"anchor":1025,"definition":1483},"Replacing useful data with prefetched data that turns out not to be needed.",{"term":1485,"anchor":1028,"definition":1486},"Predictive Gaming Memory","Figure Rocks hypothesis combining deterministic engine state, learned prediction, prefetch and measured residency policy.",{},"2.31.6","Game engines already know where the player, camera and world are going. Research shows learned GPU prefetching can predict memory pages. What happens if those ideas are combined?",{"lang":7,"title":534,"content":536,"contentJson":1491,"excerpt":1033},{"time":538,"blocks":1492,"version":1032},[1493,1496,1499,1502,1505,1508,1525,1528,1531,1534,1543,1546,1549,1559,1562,1565,1568,1579,1582,1585,1610,1613,1616,1619,1622,1625,1628,1631,1642,1645,1656,1659,1662,1665,1668,1671,1674,1679,1684,1693],{"id":541,"data":1494,"type":544,"tunes":1495},{"text":543},{},{"id":547,"data":1497,"type":552,"tunes":1498},{"body":549,"title":550,"variant":551},{},{"id":555,"data":1500,"type":552,"tunes":1501},{"body":557,"title":558,"variant":559},{},{"id":562,"data":1503,"type":566,"tunes":1504},{"title":564,"maxLevel":565,"minLevel":47},{},{"id":569,"data":1506,"type":572,"tunes":1507},{"text":571,"level":47},{},{"id":575,"data":1509,"type":617,"tunes":1524},{"rows":1510,"title":608,"layout":609,"columns":1521},[1511,1513,1515,1517,1519],{"id":579,"label":580,"values":1512},{"other":582,"memory":583},{"id":585,"label":586,"values":1514},{"other":588,"memory":589},{"id":591,"label":592,"values":1516},{"other":594,"memory":595},{"id":597,"label":598,"values":1518},{"other":600,"memory":601},{"id":603,"label":604,"values":1520},{"other":606,"memory":607},[1522,1523],{"id":612,"label":613},{"id":615,"label":616},{},{"id":620,"data":1526,"type":626,"tunes":1527},{"url":622,"title":623,"excerpt":624,"ctaLabel":625},{},{"id":629,"data":1529,"type":572,"tunes":1530},{"text":631,"level":47},{},{"id":634,"data":1532,"type":544,"tunes":1533},{"text":636},{},{"id":639,"data":1535,"type":659,"tunes":1542},{"steps":1536,"title":657,"orientation":658},[1537,1538,1539,1540,1541],{"label":643,"description":644},{"label":646,"description":647},{"label":649,"description":650},{"label":652,"description":653},{"label":655,"description":656},{},{"id":662,"data":1544,"type":572,"tunes":1545},{"text":664,"level":47},{},{"id":667,"data":1547,"type":544,"tunes":1548},{"text":669},{},{"id":672,"data":1550,"type":609,"tunes":1558},{"content":1551,"stretched":705,"withHeadings":15},[1552,1553,1554,1555,1556,1557],[676,677,678,679],[681,682,683,684],[686,687,688,689],[691,692,693,694],[696,697,698,699],[701,702,703,704],{},{"id":708,"data":1560,"type":552,"tunes":1561},{"body":710,"title":711,"variant":712},{},{"id":715,"data":1563,"type":544,"tunes":1564},{"text":717},{},{"id":720,"data":1566,"type":572,"tunes":1567},{"text":722,"level":47},{},{"id":725,"data":1569,"type":659,"tunes":1578},{"steps":1570,"title":749,"orientation":658},[1571,1572,1573,1574,1575,1576,1577],{"label":729,"description":730},{"label":732,"description":733},{"label":735,"description":736},{"label":738,"description":739},{"label":741,"description":742},{"label":744,"description":745},{"label":747,"description":748},{},{"id":752,"data":1580,"type":552,"tunes":1581},{"body":754,"title":755,"variant":756},{},{"id":759,"data":1583,"type":572,"tunes":1584},{"text":761,"level":47},{},{"id":764,"data":1586,"type":617,"tunes":1609},{"rows":1587,"title":821,"layout":609,"columns":1606},[1588,1590,1592,1594,1596,1598,1600,1602,1604],{"id":768,"label":769,"values":1589},{"known":771,"learned":772},{"id":774,"label":775,"values":1591},{"known":777,"learned":778},{"id":780,"label":781,"values":1593},{"known":783,"learned":784},{"id":786,"label":787,"values":1595},{"known":789,"learned":790},{"id":792,"label":793,"values":1597},{"known":795,"learned":796},{"id":798,"label":799,"values":1599},{"known":801,"learned":802},{"id":804,"label":805,"values":1601},{"known":807,"learned":808},{"id":810,"label":811,"values":1603},{"known":813,"learned":814},{"id":816,"label":817,"values":1605},{"known":819,"learned":820},[1607,1608],{"id":824,"label":825},{"id":827,"label":828},{},{"id":831,"data":1611,"type":572,"tunes":1612},{"text":833,"level":47},{},{"id":836,"data":1614,"type":544,"tunes":1615},{"text":838},{},{"id":841,"data":1617,"type":572,"tunes":1618},{"text":843,"level":47},{},{"id":846,"data":1620,"type":544,"tunes":1621},{"text":848},{},{"id":851,"data":1623,"type":626,"tunes":1624},{"url":853,"title":854,"excerpt":855,"ctaLabel":856},{},{"id":859,"data":1626,"type":572,"tunes":1627},{"text":861,"level":47},{},{"id":864,"data":1629,"type":544,"tunes":1630},{"text":866},{},{"id":869,"data":1632,"type":609,"tunes":1641},{"content":1633,"stretched":705,"withHeadings":15},[1634,1635,1636,1637,1638,1639,1640],[805,873,874],[876,877,878],[880,881,882],[884,885,886],[888,889,890],[892,893,894],[896,897,898],{},{"id":901,"data":1643,"type":572,"tunes":1644},{"text":903,"level":47},{},{"id":906,"data":1646,"type":659,"tunes":1655},{"steps":1647,"title":930,"orientation":658},[1648,1649,1650,1651,1652,1653,1654],{"label":910,"description":911},{"label":913,"description":914},{"label":916,"description":917},{"label":919,"description":920},{"label":922,"description":923},{"label":925,"description":926},{"label":928,"description":929},{},{"id":933,"data":1657,"type":572,"tunes":1658},{"text":935,"level":47},{},{"id":938,"data":1660,"type":544,"tunes":1661},{"text":940},{},{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erfolgreich abgerufen",{"items":1703,"source":1772,"manualIds":1773,"manualMatchedIds":1774},[1704,1710,1716,1723,1728,1734,1739,1745,1750,1755,1761,1767],{"id":1705,"slug":1706,"title":1707,"excerpt":1708,"featuredImage":14,"publishedAt":1709},"66","experience-first-why-gaming-feel-beats-specs-a-value-standard","体验至上：为何游戏感受胜过硬件参数（一种价值标准）","互联网售卖的是数字，玩家感受的是时机。这是一个实用的价值标准：体验为先，稳定其次，随后才是装备与价格。","2026-02-19T11:00:00.000Z",{"id":1711,"slug":1712,"title":1713,"excerpt":1714,"featuredImage":14,"publishedAt":1715},"402","ll-amiibo-compatible-games-complete-overview-of-bonuses-and-gameplay-integration","所有兼容amiibo的游戏——奖励与游戏玩法整合完整概览","amiibo手办可与Wii U、任天堂3DS及任天堂Switch平台上的众多任天堂游戏互动。不同游戏中的附加价值各有差异：有时奖励仅为外观装饰，有时则会直接影响游戏玩法。以下将按游戏平台分类，系统梳理兼容作品及扫描手办可解锁的核心内容。","2026-02-27T18:13:00.000Z",{"id":1717,"slug":1718,"title":1719,"excerpt":1720,"featuredImage":1721,"publishedAt":1722},"434","google-io-2026-gaming-ai-xr-and-the-next-wave-of-collectibles","Google I\u002FO 2026 与游戏：AI、XR 以及下一波收藏品浪潮","Google I\u002FO 2026并非一场典型的游戏活动，但其AI、XR、Gemini、Google Play和创意工具方面的公告指向了游戏文化的一次明显转变。本文解释了AI发现、XR伴侣层、Gemini驱动的媒体以及更快的角色可见性可能如何塑造游戏、手办、收藏品和未来的粉丝生态系统。","\u002Fuploads\u002F2026\u002F05\u002Fgoogle-io-2026-gaming-ai-xr-and-the-next-wave-of-collectibles-1779257974958-jjj07f.webp","2026-05-20T00:17:00.000Z",{"id":1724,"slug":1725,"title":1726,"excerpt":1727,"featuredImage":14,"publishedAt":1709},"53","smoothness-is-frame-pacing-why-stable-frametime-beats-raw-fps","流畅度在于帧间隔：为何稳定的帧时间胜过原始帧率","如果你的游戏在高帧率下感觉卡顿，通常是由于帧时间不均衡。首先了解什么是帧时间、导致帧时间波动的原因以及如何稳定它。",{"id":1729,"slug":1730,"title":1731,"excerpt":1732,"featuredImage":14,"publishedAt":1733},"185","hdr-tone-mapping-traps-when-dynamic-settings-destroy-detail","HDR色调映射陷阱：当“动态”设置破坏细节时","许多HDR问题实为色调映射问题。“动态”模式可能压暗高光或将黑色提亮成灰雾。以此基准保持细节清晰可辨。","2026-02-20T16:00:00.000Z",{"id":1735,"slug":1736,"title":1737,"excerpt":1738,"featuredImage":14,"publishedAt":1709},"17","fix-packet-loss-the-hidden-cause-of-random-online-problems","修复数据包丢失：在线问题“随机”出现的隐藏原因","丢包体验如同随机延迟、命中滞后与动作卡顿。本指南将定位丢包发生环节，并明确优先修复项。",{"id":1740,"slug":1741,"title":1742,"excerpt":1743,"featuredImage":14,"publishedAt":1744},"398","upcoming-nintendo-amiibo-new-figures-release-windows-and-functional-significance","即将推出的任天堂amiibo——新模型、发售窗口与功能意义","amiibo是配备NFC芯片的实体手办，可与任天堂游戏机进行通信。在兼容游戏中，它们能解锁游戏内物品、装饰元素或小型游戏功能。本目录概览主要关注官方已公布的即将推出的amiibo，包括其预计发布时间窗口、功能价值以及在更广泛的产品批次中的定位。","2026-02-27T15:54:00.000Z",{"id":1746,"slug":1747,"title":1748,"excerpt":1749,"featuredImage":14,"publishedAt":1709},"15","fix-bufferbloat-stop-lag-spikes-while-downloading-or-streaming","修复缓冲区膨胀：下载或流媒体时告别延迟高峰","如果有人下载或流媒体时ping值飙升，那就是缓冲区膨胀。本操作指南可在路由器层面修复这种峰值问题。",{"id":1751,"slug":1752,"title":1753,"excerpt":1754,"featuredImage":14,"publishedAt":1709},"62","oled-vs-ips-vs-va-for-gaming-what-you-gain-and-what-you-lose","OLED vs IPS vs VA 游戏显示器对比：优势与劣势解析","面板类型是权衡：动态、对比度、视角与延迟表现。了解影响体验的关键，而非营销噱头。",{"id":1756,"slug":1757,"title":1758,"excerpt":1759,"featuredImage":14,"publishedAt":1760},"245","qos-myths-for-gaming-what-helps-what-hurts-and-the-real-priority","游戏中的QoS迷思：哪些有益，哪些有害，以及真正的优先级","QoS并非一个神奇的“游戏”开关。良好的队列管理能在负载下降低延迟，而糟糕的QoS则会增加抖动或破坏公平性。以下是理解这一概念的稳定思路。","2026-02-20T23:55:00.000Z",{"id":1762,"slug":1763,"title":1764,"excerpt":14,"featuredImage":1765,"publishedAt":1766},"456","what-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split","一台类似Spark的游戏PC究竟会带来什么改变？统一内存、AI计算与经典的GPU分工","\u002Fuploads\u002F2026\u002F10\u002Fwhat-would-a-spark-like-gaming-pc-actually-change-unified-memory-ai-compute-and-the-classic-gpu-split-1792340021752-bia7n6.webp","2026-10-18T12:12:00.000Z",{"id":1768,"slug":1769,"title":1770,"excerpt":1771,"featuredImage":14,"publishedAt":1709},"122","ergonomics-for-long-sessions-comfort-that-prevents-fatigue","长时间使用的工效学设计：舒适度预防疲劳","舒适即是性能。运用这一简单的人体工学基准，可有效减轻疲劳感，保持操控一致性，助您更持久畅玩而无痛感。","fallback",[],[]]