论文标题
计算物理的内联矢量压缩
Inline Vector Compression for Computational Physics
论文作者
论文摘要
在三个维度的单精度向量提出了一种新型的内联数据压缩方法。该方法的主要应用是用于加速吞吐量受内存带宽绑定的计算物理计算。该方案采用球形极坐标，角度定量和定制的浮点数表示，以达到1.5的固定压缩比。考虑了这种方法的各向异性，以及构成和分数分裂技术以提高表示形式的效率。我们在高阶计算流体动力学的背景下以数值评估该方案。对于等凝的对流涡流和泰勒 - 绿色涡流测试用例，结果都与没有压缩的结果相当。评估了NVIDIA TITAN V GPU上的矢量添加核的性能；证明可以实现1.5的加速。
A novel inline data compression method is presented for single-precision vectors in three dimensions. The primary application of the method is for accelerating computational physics calculations where the throughput is bound by memory bandwidth. The scheme employs spherical polar coordinates, angle quantisation, and a bespoke floating-point representation of the magnitude to achieve a fixed compression ratio of 1.5. The anisotropy of this method is considered, along with companding and fractional splitting techniques to improve the efficiency of the representation. We evaluate the scheme numerically within the context of high-order computational fluid dynamics. For both the isentropic convecting vortex and the Taylor--Green vortex test cases, the results are found to be comparable to those without compression. Performance is evaluated for a vector addition kernel on an NVIDIA Titan V GPU; it is demonstrated that a speedup of 1.5 can be achieved.
