APEnet+：一个在商品簇上实现千万亿次规模格点量子色动力学模拟的3D环形网络
APEnet+: a 3D toroidal network enabling Petaflops scale Lattice QCD simulations on commodity clusters
摘要：
许多科学计算需要多节点并行来匹配不断增长的空间（内存）和时间（速度）需求。使用GPU作为加速器为程序员引入了另一个复杂性级别，并且由于复杂的存储器层次结构，可能会导致大量开销。此外，一流的问题可能很容易使用超过千万亿次的持续计算能力，需要数千个GPU与一些并行编程模型协调。在这里，我们描述了APEnet+，我们的互连的新一代，可扩展到数以万计的节点与线性成本，从而提高大型集群的性价比。该项目的目标是开发Apelink+主机适配器，该适配器具有低延迟、高带宽直连网络、最先进的链路线速和PCIe X8 gen2主机接口。它具有对RDMA编程模型的硬件支持和GPU网络的实验加速。一个Linux内核驱动程序，一组低级的RDMA API和一个OpenMPI库驱动程序都是可用的，允许轻松移植标准应用程序。最后，我们给出了未来的工作和预期的发展的见解。
英文摘要：
Many scientific computations need multi-node parallelism for matching up both space (memory) and time (speed) ever-increasing requirements. The use of GPUs as accelerators introduces yet another level of complexity for the programmer and may potentially result in large overheads due to the complex memory hierarchy. Additionally, top-notch problems may easily employ more than a Petaflops of sustained computing power, requiring thousands of GPUs orchestrated with some parallel programming model. Here we describe APEnet+, the new generation of our interconnect, which scales up to tens of thousands of nodes with linear cost, thus improving the price/performance ratio on large clusters. The project target is the development of the Apelink+ host adapter featuring a low latency, high bandwidth direct network, state-of-the-art wire speeds on the links and a PCIe X8 gen2 host interface. It features hardware support for the RDMA programming model and experimental acceleration of GPU networking. A Linux kernel driver, a set of low-level RDMA APIs and an OpenMPI library driver are available, allowing for painless porting of standard applications. Finally, we give an insight of future work and intended developments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
