Data-type specific cache compression in GPGPUs

Abstract

In this paper, we evaluate compressibility of L1 data caches and L2 cache in general-purpose graphics processing units (GPGPUs). Our proposed scheme is geared toward improving performance and power of GPGPUs through cache compression. GPGPUs are throughput-oriented devices which execute thousands of threads simultaneously. To handle working set of this massive number of threads, modern GPGPUs exploit several levels of caches. GPGPU design trend shows that the size of caches continues to grow to support even more thread level parallelism. We propose using cache compression to increase effective cache capacity, improve performance, and reduce power consumption in GPGPUs. Our work is motivated by the observation that the values within a cache block are similar, i.e., the arithmetic difference of two successive values within a cache block is small. To reduce data redundancy in L1 data caches and L2 cache, we use low-cost and implementation-efficient base-delta-immediate (BDI) algorithm. BDI replaces a cache block with a base and an array of deltas where the combined size of the base and deltas is less than the original cache block. We also study locality of fields in integer and floating-point numbers. We found that entropy of fields varies across different data types. Based on entropy, we offer different BDI compression schemes for integer and floating-point numbers. We augment a simple, yet effective, predictor that determines type of values dynamically in hardware and without the help of a compiler or a programmer. Evaluation results show that on average, cache compression improves performance by 8% and saves energy of caches by 9%.