Optimizations of Two Compute-bound Scientific Kernels on the SW26010 Many-core Processor

摘要

The home-grown SW26010 many-core processor enabled the production of China's first independently developed number-one ranked supercomputer - the Sunway TaihuLight. The design of the limited off-chip memory bandwidth, however, renders the SW26010 a highly memory-bound processor. To compensate for this limitation, the processor was designed with a unique hardware feature, "Register Level Communication" (RLC), to share register data among its 8 × 8 computing processing elements (CPEs) via a 2D on-chip network. Such a radical architecture has sparked global researchers' concerns regarding the programming challenges this may cause. To address these concerns, we adopted two compute-bound scientific kernels as benchmarks to identify the potential programming challenges. The first kernel is double-precision general matrix-multiplication (DGEMM). An RLC-friendly algorithm was designed for this kernel to reuse the data that already reside in the registers of 64 CPEs. This novel optimization enables the kernel to achieve up to 88.7% efficiency in one core group of the SW26010. This paper reveals, for the first time, the details of how the highly efficient DGEMM is implemented on the home-grown processor. The second kernel that we used is N-body. Due to the inefficient hardware support for transcendental operations on the SW26010, we replaced the reciprocal square root (rsqrt) instruction of N-body with a software routine to tackle the problem. Based on the programming challenges identified through these two optimized kernels, we proposed a three-level programming guideline for the SW26010. The paper concludes with our crucial finding that the critical step towards bridging the ninja performance gap on the SW26010 is to design an RLC-friendly algorithm to increase arithmetic intensity.