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Applications running on clusters of shared-memory computers are often implemented using OpenMP+MPI. Productivity can be vastly improved using task-based programming, a paradigm where the user expresses the data and control-flow relations between tasks, offering the runtime maximal freedom to place and schedule tasks. While productivity is increased, high-performance execution remains challenging:...
The complexity of High Performance Computing (HPC) systems is increasing in the number of components and their heterogeneity. Interactions between software and hardware involve many different aspects which are typically not transparent to scientific programmers and system architects. Therefore, predicting the behavior of current scientific applications on future HPC infrastructures is a challenging...
GPUs have emerged as general-purpose accelerators in high-performance computing (HPC) and scientific applications. However, the reliability characteristics of GPU applications have not been investigated in depth. While error propagation has been extensively investigated for non-GPU applications, GPU applications have a very different programming model which can have a significant effect on error propagation...
Stencil computation has many applications in science and engineering, thus many optimization techniques such as temporal blocking have been developed. They are, however, rarely used in real-world applications, since a large amount of careful programming is required for even the simplest of stencils. We introduce Formura, a domain specific language that provides easy access to optimized stencil computations...
We evaluate the power and performance of the Rodinia benchmark suite using the Altera SDK for OpenCL targeting a Stratix V FPGA against a modern CPU and GPU. We study multiple OpenCL kernels per benchmark, ranging from direct ports of the original GPU implementations to loop-pipelined kernels specifically optimized for FPGAs. Based on our results, we find that even though OpenCL is functionally portable...
Fairness and efficiency are two important concerns for users in a shared computer system, and there tends to be a tradeoff between them. Heterogeneous computing poses new challenging issues on the fair allocation of computational resources among users due to the availability of different kinds of computing devices (e.g., CPU and GPU). Prior work either considers the fair resource allocation separately...
Hardware architecture is increasingly complex, urging the development of asynchronous runtime systems with advance resource and locality management supports. However, these supports may come at the cost of complicating the user interface while programming remains one of the major constraints to wide adoption of asynchronous runtimes in practice. In this paper, we propose a solution that leverages...
Adaptive Mesh Refinement methods reduce computational requirements of problems by increasing resolution for only areas of interest. However, in practice, efficient AMR implementations are difficult considering that the mesh hierarchy management must be optimized for the underlying hardware. Architecture complexity of GPUs can render efficient AMR to be particularity challenging in GPU-accelerated...
In this paper, we propose a framework that translates OpenMP 4.0 accelerator directives to OpenCL. By translating an OpenMP program to an OpenCL program, the program can be executed on any hardware platform that supports OpenCL. We also propose a run-time optimization technique that automatically eliminates unnecessary data transfers between the host and the target accelerator. It exploits the page-fault...
Over the last decade, CUDA and the underlying GPU hardware architecture have continuously gained popularity in various high-performance computing application domains such as climate modeling, computational chemistry, or machine learning. Despite this popularity, we lack a single coherent programming model for GPU clusters. We therefore introduce the dCUDA programming model, which implements device-side...
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