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GPUs are employed to accelerate scientific applications however they require much more programming effort from the programmers particularly because of the disjoint address spaces between the host and the device. OpenACC and OpenMP 4.0 provide directive based programming solutions to alleviate the programming burden however synchronous data movement can create a performance bottleneck in fully taking...
Programming models like CUDA, OpenMP, OpenACC and OpenCL are designed to offload compute-intensive workloads to accelerators efficiently. However, the naive offload model, which synchronously copies and executes in sequence, requires extensive hand-tuning of techniques, such as pipelining to overlap computation and communication. Therefore, we propose an easy-to-use, directive-based pipelining extension...
Graphics Processing Units (GPUs) have been successfully used to accelerate scientific applications due to their computation power and the availability of programming languages that make more approachable writing scientific applications for GPUs. However, since the programming model of GPUs requires offloading all the data to the GPU memory, the memory footprint of the application is limited to the...
GPU programmers suffer from programmer-managed GPU memory because both performance and programmability heavily depend on GPU memory allocation and CPU-GPU data transfer mechanisms. To improve performance and programmability, programmers should be able to place only the data frequently accessed by GPU on GPU memory while overlapping CPU-GPU data transfers and GPU executions as much as possible. However,...
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