一个重要的融合HPC代码基准的单节点和多节点性能比较
Comparing single-node and multi-node performance of an important fusion HPC code benchmark
摘要：
聚变模拟传统上需要使用领先规模的高性能计算（HPC）资源，以产生物理学的进步。许多GPU计算节点的计算和内存容量的显著改进现在允许一些曾经需要多节点设置的问题也可以在单个节点上解决。在可能的情况下，增加的互连带宽可以导致更高的科学吞吐量，特别是对于通信繁重的应用。在本文中，我们分析了性能的融合仿真工具CGYRO，欧拉回旋湍流求解器的设计和优化的碰撞，电磁，多尺度模拟，这是广泛应用于融合研究界。由于问题的性质，应用程序必须在一个大型多维计算网格上工作，需要在计算过程之间频繁交换大量数据。特别是，我们证明了平均规模的nl03基准CGYRO模拟可以在具有16个A100 GPU的单个Google Cloud实例上以可接受的速度运行，优于8个NERSC Perlmutter Phase1节点，16个ORNL Summit节点和256个NERSC Cori节点。从多节点到单节点GPU设置，我们使用不到一半的GPU数量获得了相当的模拟时间。然而，由于GPU内存容量需求，较大的基准测试问题仍然需要多节点HPC设置，因为在撰写本文时，没有供应商提供具有足够GPU内存设置的节点。然而，即将推出的外部NVIDIA确实承诺为多达256个NVIDIA GPU提供几乎等同的解决方案。
英文摘要：
Fusion simulations have traditionally required the use of leadership scale High Performance Computing (HPC) resources in order to produce advances in physics. The impressive improvements in compute and memory capacity of many-GPU compute nodes are now allowing for some problems that once required a multi-node setup to be also solvable on a single node. When possible, the increased interconnect bandwidth can result in order of magnitude higher science throughput, especially for communication-heavy applications. In this paper we analyze the performance of the fusion simulation tool CGYRO, an Eulerian gyrokinetic turbulence solver designed and optimized for collisional, electromagnetic, multiscale simulation, which is widely used in the fusion research community. Due to the nature of the problem, the application has to work on a large multi-dimensional computational mesh as a whole, requiring frequent exchange of large amounts of data between the compute processes. In particular, we show that the average-scale nl03 benchmark CGYRO simulation can be run at an acceptable speed on a single Google Cloud instance with 16 A100 GPUs, outperforming 8 NERSC Perlmutter Phase1 nodes, 16 ORNL Summit nodes and 256 NERSC Cori nodes. Moving from a multi-node to a single-node GPU setup we get comparable simulation times using less than half the number of GPUs. Larger benchmark problems, however, still require a multi-node HPC setup due to GPU memory capacity needs, since at the time of writing no vendor offers nodes with a sufficient GPU memory setup. The upcoming external NVSWITCH does however promise to deliver an almost equivalent solution for up to 256 NVIDIA GPUs.
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。
