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.

中文翻译：

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计算物理的内联向量压缩

针对三维单精度向量提出了一种新的内联数据压缩方法。该方法的主要应用是加速计算物理计算，其中吞吐量受内存带宽限制。该方案采用球面极坐标、角度量化和幅度的定制浮点表示，以实现 1.5 的固定压缩比。考虑了该方法的各向异性，以及压缩扩展和分数分裂技术，以提高表示的效率。我们在高阶计算流体动力学的背景下以数值方式评估该方案。对于等熵对流涡旋和泰勒-格林涡旋测试案例，发现结果与没有压缩的结果相当。在 NVIDIA Titan V GPU 上评估向量加法内核的性能；结果表明，可以实现 1.5 的加速。