Dual Pattern Compression Using Data-Preprocessing for Large-Scale GPU Architectures

Abstract

Graphics Processing Units (GPUs) have been widely accepted for diverse general purpose applications due to a massive degree of parallelism. The demand for large-scale GPUs processing a large volume of data with high throughput has been rising rapidly. However, in large-scale GPUs, a bandwidth-efficient network design is challenging. Compression techniques are a practical remedy to effectively increase network bandwidth by reducing data size transferred. We propose a new simple compression mechanism, Dual Pattern Compression (DPC), that compresses only two patterns with a very low latency. The simplicity of compression/decompression is achieved through data remapping and data-type-aware data preprocessing which exploits bit-level data redundancy. The data type is detected during runtime. We demonstrate that our compression scheme effectively mitigates the network congestion in a large-scale GPU. It achieves IPC improvement by 33% on average (up to 126%) across various benchmarks with average space savings ratios of 61% in integer, 46% (up to 72%) in floating-point and 23% (up to 57%) in character type benchmarks.