Analysis of GEMV Kernel Computations by Address Mapping Approaches Based on GPU and PIM Architectures

Abstract

Processing-in-Memory (PIM) is an architectural approach that can overcome bandwidth limitations between host processors and off-chip memory. PIM can improve data computation performance by exploiting high internal bandwidth and parallel computations within a memory module. Therefore, it is expected that PIMs can be paired with high-performance processors such as GPUs to achieve overall performance improvements. However, due to differences in memory address mapping schemes between PIM and GPU architectures, applying PIM’s address mapping method directly to GPUs may result in a decrease in overall performance. In this paper, we analyze the performance impact of PIM’s address mapping schemes on GPU using memory-intensive general matrix-vector product (GEMV) kernels. Our evaluation results exhibit that PIM’s address mapping schemes degrade performance and memory bandwidth on GPUs, indicating that differences in mapping schemes could potentially cause performance degradation in GPU-PIM architectures.