Global Shared Memory Design for Multi-GPU Graphics Cards on Personal Supercomputer

Abstract

When programming CUDA or OpenCL on multi-GPU systems, the programmers usually expect the GPUs on the same system can communicate fast with each other. For instance, they hope a device memory copy from GPU1s memory to GPU2s memory can be done inside the graphics card, and needn’t to employ the PCIE, which is in relative low speed. In this paper, we propose an idea to add a multi-channel memory to the multi-GPU board, and this memory is only for transferring data between different GPUs. This multi-channel memory should have multiple interfaces, including one common interface shared by different GPUs, which is connected with a FPGA arbitration circuit and several other interfaces connected with dedicated GPUs frame buffer independently. To distinguish the shared memory of a stream multiprocessor, we call this memory Global Shared Memory. We analyze the performance improvement expectation with this global shared memory, with the case of accelerating computer tomography algebraic reconstruction on multi-GPU.