Abstract
We present VectorPU, a C++ based programming framework providing high-level and efficient unified memory access on heterogeneous systems, in particular GPU-based systems. VectorPU consists of a light-weight runtime library providing a generic, smart data-container abstraction for transparent software caching of array operands with programmable memory coherence, and a light-weight component model realized by macro-based data access annotations. VectorPU thereby enables a flexible unified memory view with data transfer and device memory management abstracted away from programmers, while keeping the efficiency of expert-written code with manual data movement and memory management. We provide a prototype of VectorPU for (CUDA) GPU-based systems, and show that it can achieve 1.40× to 13.29× speedup over good quality code using Nvidia's Unified Memory by experiments on several machines ranging from laptops to supercomputer nodes, with Kepler and Maxwell GPUs. We also show the expressiveness and wide applicability of VectorPU, and its low overhead and equal efficiency compared to expert-written code.
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