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Programming hybrid CPU-GPU clusters is hard. This paper addresses this difficulty and presents the design and runtime implementation of <bold/><bold>Unicorn</bold><bold/>—a parallel programming model for hybrid CPU-GPU clusters. In particular, this paper proves that efficient distributed shared memory style programing is possible and its simplicity can be retained across CPUs...
More and more computationally intensive scientific applications make use of hardware accelerators like general purpose graphics processing units (GPGPUs). Compared to software development for typical multi-core processors their programming is fairly complex and needs hardware specific optimizations to utilize the full computing power. To achieve high performance, critical parts of a program have to...
GPUs and other accelerators are available on many different devices, while GPGPU has been massively adopted by the HPC research community. Although a plethora of libraries and applications providing GPU support are available, the need of implementing new algorithms from scratch, or adapting sequential programs to accelerators, will always exist. Writing CUDA or OpenCL codes, although an easier task...
User-friendly parallel programming environments, such as CUDA and OpenCL are widely used for accelerators. They provide programmers with useful APIs, but the APIs are still low level primitives. Therefore, in order to apply communication optimization techniques, such as double buffering techniques, programmers have to manually write the programs with the primitives. Manual communication optimization...
Clusters of GPUs are emerging as a new computational scenario. Programming them requires the use of hybrid models that increase the complexity of the applications, reducing the productivity of programmers. We present the implementation of OmpSs for clusters of GPUs, which supports asynchrony and heterogeneity for task parallelism. It is based on annotating a serial application with directives that...
New high performance computing (HPC) applications recently have to face scalability over an increasing number of nodes and the programming of special accelerator hardware. Hybrid composition of large computing systems leads to a new dimension in complexity of software development. This paper presents a novel approach to gain insight into accelerator interaction and utilization without any changes...
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