Scaling large-data computations on multi-GPU accelerators

Modern supercomputers rely on accelerators to speed up highly parallel workloads. Intricate programming models, limited device memory sizes and overheads of data transfers between CPU and accelerator memories are among the open challenges that restrict the widespread use of accelerators. First, this paper proposes a mechanism and an implementation to automatically pipeline the CPU-GPU memory channel so as to overlap the GPU computation with the memory copies, alleviating the data transfer overhead. Second, in doing so, the paper presents a technique called Computation Splitting, COSP, that caters to arbitrary device memory sizes and automatically manages to run out-of-card OpenMP-like applications on GPUs. Third, a novel adaptive runtime tuning mechanism is proposed to automatically select the pipeline stage size so as to gain the best possible performance. The mechanism adapts to the underlying hardware in the starting phase of a program and chooses the pipeline stage size. The techniques are implemented in a system that is able to translate an input OpenMP program to multiple GPUs attached to the same host CPU. Experimentation on a set of nine benchmarks shows that, on average, the pipelining scheme improves the performance by 1.49x, while limiting the runtime tuning overhead to 3% of the execution time