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<p>文章的主要贡献是对异质性硬件（DPU、FPGA、GPU）的serverless支持。主要设计亮点</p>
<ul>
<li>添加了一个Shim层来对不同硬件的OS和接口进行统一调用和管理</li>
<li>vectorized
sandbox来支持对FPGA的function的操作。主要就是用一个sandbox
vector来代替一个sanbox</li>
</ul>
<p>此外还搞了一个类似Catalyzer的cfork，但是cfork使用runtime实现的</p>
<h1 id="background">Background</h1>
<p>为什么需要异质硬件？</p>
<ul>
<li>instance个数限制：需要DPU来提升density（1CPU 1000 instances；+1 DPU
+256 instances）</li>
<li>许多特殊的应用都需要accelerator：FPGA加速</li>
</ul>
<p>几个挑战</p>
<ol type="1">
<li>multi-OS。不同硬件有不同的OS</li>
<li>abstract。不同硬件如何提供统一抽象</li>
<li>通信</li>
</ol>
<h1 id="abstraction">Abstraction</h1>
<p>主要包括两个部分：XPU-shim和vectorized sandbox</p>
<h2 id="xpu-shim">XPU-shim</h2>
<p>主要有以下三个feature</p>
<ul>
<li>在所需要交互的OS之上，利用vectorized
sandbox的接口，实现对函数的管理</li>
<li>提供XPUcalls，类似系统调用</li>
<li>支持通信</li>
</ul>
<h3 id="distributed-capability">Distributed Capability</h3>
<p>XPU-Shim使用分布式对象和Capability为用户空间的应用程序维护全局资源和状态。
目前，我们已经定义了以下两个分布式对象。</p>
<ul>
<li>CAP_Group是记录进程所有Capability的对象</li>
<li>IPC是进程间的连接对象</li>
</ul>
<p>XPU-Shim只利用能力系统来管理不同PU之间的权限和通信</p>
<p>同时还包括一个全局唯一ID和CAP_GROUP记载的权限list</p>
<h3 id="neighbor-ipc">Neighbor IPC</h3>
<p>系统里一共有三种IPC</p>
<ul>
<li>Remote IPC：访问不同机器，通过network</li>
<li>Local IPC：同一个机器上的同一个硬件</li>
<li>Neighbor IPC：同一个机器访问不同硬件，比如CPU访问DPU</li>
</ul>
<h3 id="miscellaneous-xpu-operations">Miscellaneous XPU Operations</h3>
<p>此外还支持跨PU的spawn</p>
<h2 id="vectorized-sandbox">Vectorized sandbox</h2>
<p>直接在FPGA上实现容器OCI有两个挑战</p>
<p>首先，OCI接口在FPGA等异构设备上的可扩展性较差。与通用的PU不同，FPGA一次只能刷新一个image。
此外，这也导致了更高的启动延迟，因为我们需要为大多数请求重新编程FPGA设备。</p>
<p>第二，明确地删除沙盒会产生非实质性的费用，而这些费用在这些情况下是不必要的。
在CPU或DPU的情况下，无服务器的运行时间应该明确地删除沙盒以节省硬件资源。
然而，在FPGA中，刷新的函数不会占用任何资源，并且可以在创建新的FPGA少服务器函数时轻松替换。这两个限制促使了矢量沙盒的抽象化。</p>
<p>为此做了以下三个扩展</p>
<ul>
<li>create不再是创建一个sandbox，而是创造一个sandbox的向量</li>
<li>start也是开始一个向量，这里面的都并行执行</li>
<li>delete不真正擦除，只是标记状态</li>
</ul>
<h1 id="design">Design</h1>
<ul>
<li>Programming
models。类似Lambda的。DPU和CPU的编程模型一样。FPGA有自己的一套</li>
<li>container fork (cfork)。继承了Catalyzer中sandbox
fork的思想，从预先准备好的模板容器（在CPU和DPU上）生成新的实例，但克服了三个新的挑战
<ul>
<li>首先，一个容器可能包含多线程甚至多进程，这些线程很难被Unix
fork正确和有效地克隆。Catalyzer依赖于underlying的管理程序（即gVisor）来提供fork整个沙盒的能力，这对于没有管理程序层的Molecule是不适用的。为了解决这个问题，cfork提出了可fork的语言运行时：一个无服务器函数的包装器，可以重新fork多线程实例。runtime将暂时把所有线程合并成一个线程，把多线程的上下文保存在内存中，并在cfork之后把它扩展成一个多线程的。</li>
<li>其次，Molecule需要将fork的函数实例从模板容器中迁移到新的容器中进行隔离。为了满足这一需求，Molecule将为fork的实例准备一个新的容器（称为函数容器）。在cfork过程中，fork的运行时将根据函数的配置重新配置其命名空间和cgroup，然后加载函数的代码（如果有的话，还有依赖关系），并建立与Molecule的连接。之后，Molecule可以将请求分配给子实例，而子实例在函数容器中执行这些请求。默认情况下，Molecule为所有使用相同语言的函数准备了通用模板容器，例如，一个Python模板用于所有Python函数。Molecule可以为热点函数启动一个带有代码和依赖关系的专用模板，以进一步减少延迟。</li>
<li>第三，cfork应该支持异构计算机上的多操作系统。为了在多个PU上fork新的实例，每个PU需要为每个语言的运行时间准备一个模板容器。之后，Molecule可以利用nIPC请求邻近的PU创建函数容器并fork新的实例。新的实例将利用nIPC来获取请求和返回响应。</li>
</ul></li>
<li>Caching FPGA function
instances。FPGA上没有fork，因此用cache，实现准备好image</li>
<li>nIPC-based DAG call。使用全双工的FIFO，通过XPU-shim直接连接。</li>
<li>XPUcall优化。XPU-Shim是一个os上的单独的程序，因此需要IPC。为此提出了两个加速方案
<ul>
<li>MPSC（Multi-Producer
Single-Consumer）队列上进行轮询，并依靠IPC来通知进程进行XPUcall响应的情况。</li>
<li>MPSC队列只用于通知XPU-Shim哪个进程发出了XPUcalls。再有另一个per-process的share
memory用来被user function轮询来得到invocation information</li>
</ul></li>
<li>Inter-PU
synchronization。一共有三种方式：不同步、即时同步、Lazy同步</li>
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