The Barcelona Supercomputing Center (BSC) has recently conducted a performance evaluation of SpGEMM on RISC-V vector processors. SpGEMM stands for sparse general matrix multiplication and is a key operation in many scientific computing applications. The evaluation was conducted using the BSC’s MareNostrum supercomputer, which is equipped with the latest RISC-V vector processors. The results of the evaluation are very encouraging and demonstrate the potential of RISC-V vector processors for high-performance computing.
The evaluation was conducted by running SpGEMM on a range of different RISC-V vector processors. The performance of the processors was measured in terms of their speedup compared to a single-core processor. The results showed that the RISC-V vector processors were able to achieve a speedup of up to 8.5x compared to the single-core processor. This is a significant improvement and demonstrates the potential of RISC-V vector processors for high-performance computing.
The evaluation also showed that the performance of the RISC-V vector processors was highly dependent on the type of matrix being multiplied. For example, when multiplying a sparse matrix, the speedup was much higher than when multiplying a dense matrix. This indicates that the RISC-V vector processors are well suited for applications that involve sparse matrix multiplication.
Overall, the evaluation conducted at the BSC demonstrates the potential of RISC-V vector processors for high-performance computing. The results show that they can achieve significant speedups compared to single-core processors, and that their performance is highly dependent on the type of matrix being multiplied. This makes them an attractive option for applications that involve sparse matrix multiplication.
The BSC’s evaluation of SpGEMM on RISC-V vector processors is an important step towards realizing the potential of these processors for high-performance computing. The results of this evaluation demonstrate that RISC-V vector processors are well suited for applications that involve sparse matrix multiplication, and that they can achieve significant speedups compared to single-core processors. This makes them an attractive option for many scientific computing applications.
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