We present a performance and portability evaluation of three well-established astrophysical production codes, namely iPIC3D, PLUTO, and OpenGGCM, on a Sophgo SG2044 RISC-V processor (part of the Monte Cimone cluster), with comparisons to AMD EPYC 9554 (x86) and NVIDIA GH200 Grace (ARM) systems. These applications represent memory-bound, compute-bound, and hybrid workloads, respectively. Numerical correctness is verified across all platforms, confirming portability. RISC-V shows consistently lower performance, with slowdowns of about $3-6\times$ relative to x86 and $5-9\times$ relative to ARM. The gap is mainly due to limited memory bandwidth, shared cache constraints, narrower 128-bit vector units, and lower clock frequency, but also less-mature auto-vectorization capability of the GNU compiler suite. Memory-bound kernels are the most affected, where early bandwidth saturation and L2 cache contention reduce scalability at higher thread counts. Hybrid MPI+OpenMP configurations reveal a trade-off between memory contention and communication overhead, with intermediate configurations achieving the best performance. These results suggest that RISC-V is capable of supporting scientific workloads; however, additional improvements in both hardware and compiler technology, particularly in auto-vectorization, are required to achieve competitive performance.

翻译：我们评估了三个成熟的天体物理生产代码——iPIC3D、PLUTO和OpenGGCM——在算能SG2044 RISC-V处理器（蒙特西莫内集群的一部分）上的性能与可移植性，并与AMD EPYC 9554（x86）和NVIDIA GH200 Grace（ARM）系统进行了对比。这些应用分别代表内存受限、计算受限和混合负载类型。所有平台上的数值正确性均得到验证，证实了可移植性。RISC-V的性能始终较低，相对于x86减速约$3-6$倍，相对于ARM减速约$5-9$倍。这一差距主要源于有限的内存带宽、共享缓存限制、较窄的128位向量单元以及较低的时钟频率，同时也是GNU编译器套件自动向量化能力尚不成熟所致。内存受限内核受影响最大，早期带宽饱和与L2缓存争用限制了高线程数下的可扩展性。混合MPI+OpenMP配置揭示了内存争用与通信开销之间的权衡，中间配置取得了最佳性能。这些结果表明RISC-V能够支持科学工作负载；然而，要获得具有竞争力的性能，仍需在硬件与编译器技术（尤其是自动向量化）方面进行额外改进。