A CPU-GPGPU Scheduler Based on Data Transmission Bandwidth of Workload

With the continuous development of GPUs, modern general-purpose computation on GPUs (GPGPUs) is providing growing parallelism to general programs besides graphics applications. However, for those programs that involve both CPU and GPU, the data transmission bandwidth between them may become bottleneck that prevents GPU from fully exploiting its parallel computing capacity. As to avoid the defect, we try to reduce the data transmission by keeping part of the computation tasks on the CPU side other than sending all the data over to the GPU and process there. In this way the computation is done on CPU and GPU in parallel, and therefore also reduces overall process time. In order to split the computation workload in a systematic approach, we try to divide the corresponding data into chunks of proper size. We experimented our data dividing and heterogeneous memory scheduling with 2 benchmarks. The matrix multiplication is more than 30% faster, and the k means2D is nearly 10% faster, than running solely in GPU.