Big data streaming applications require utilization of heterogeneous parallel computing systems, which may comprise multiple multi-core CPUs and many-core accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such systems require advanced knowledge of several hardware architectures and device-specific programming models, including OpenMP and CUDA. In this paper, we present HSTREAM, a compiler directive-based language extension to support programming stream computing applications for heterogeneous parallel computing systems. HSTREAM source-to-source compiler aims to increase the programming productivity by enabling programmers to annotate the parallel regions for heterogeneous execution and generate target specific code. The HSTREAM runtime automatically distributes the workload across CPUs and accelerating devices. We demonstrate the usefulness of HSTREAM language extension with various applications from the STREAM benchmark. Experimental evaluation results show that HSTREAM can keep the same programming simplicity as OpenMP, and the generated code can deliver performance beyond what CPUs-only and GPUs-only executions can deliver.
Recent versions of OpenMP and OpenACC support offloading computations to accelerating devices @cite_18 . Offloading to a single accelerating device can be easily achieved by using simple compiler-directives, whereas offloading to multiple devices requires additional programming effort. HSTREAM enables developers to use high-level compiler directives (similar to OpenMP and OpenACC) to develop applications that can be executed on multiple heterogeneous devices, such as multi-core CPUs, NVIDIA GPUs, and Intel Xeon Phi accelerators.
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Heterogeneous computing come with tremendous potential and is a leading candidate for scientific applications that are becoming more and more complex. Accelerators such as GPUs whose computing momentum is growing faster than ever offer application performance when compute intensive portions of an application are offloaded to them. It is quite evident that future computing architectures are moving towards hybrid systems consisting of multi-GPUs and multi-core CPUs. A variety of high-level languages and software tools can simplify programming these systems. Directive-based programming models are being embraced since they not only ease programming complex systems but also abstract low-level details from the programmer. We already know that OpenMP has been making programming CPUs easy and portable. Similarly, a directive-based programming model for accelerators is OpenACC that is gaining popularity since the directives play an important role in developing portable software for GPUs. A combination of OpenMP and OpenACC, a hybrid model, is a plausible solution to port scientific applications to heterogeneous architectures especially when there is more than one GPU on a single node to port an application to. However OpenACC meant for accelerators is yet to provide support for multi-GPUs. But using OpenMP we could conveniently exploit features such as for and section to distribute compute intensive kernels to more than one GPU. We demonstrate the effectiveness of this hybrid approach with some case studies in this paper.