high performance computing on graphics processing units: hgpu.org

Dynamic control flow is an important technique often used to design expressive and efficient deep learning computations for applications such as text parsing, machine translation, exiting early out of deep models and so on. However, the resulting control flow divergence makes batching, an important performance optimization, difficult to perform manually. In this paper, we present […]

While domain-specific HPC software packages continue to thrive and are vital to many scientific communities, a general purpose high-productivity GPU cluster programming model that facilitates experimentation for non-experts remains elusive. We demonstrate how Celerity, a high-level C++ programming model for distributed accelerator computing based on the open SYCL standard, allows for the quick development of […]

Java is very powerful, but in Deep Learning field, its capabilities probably has not been sufficiently exploited. Compared to the Java-based deep-learning-frameworks, the Python-based (PyTorch, TensorFlow, etc) are undoubtedly the mainstream, due to their easy-to-use, flexibility and better ecosystem. Dragon-Alpha is a Java-based Tensor Computing Framework, with easy-to-use, high-scalability and high-performance, trying to break Java’s […]

The current trend of ever-increasing performance in high performance computing (HPC) applications comes with tremendous growth in energy consumption. Because existing libraries are mainly concerned with performance, they do not make efficient use of heterogeneous computing systems, resulting in energy inefficiency. Hence, improving the energy efficiency of critical applications running on HPC systems is necessary […]

NVIDIA has been the main provider of GPU hardware in HPC systems for over a decade. Most applications that benefit from GPUs have thus been developed and optimized for the NVIDIA software stack. Recent exascale HPC systems are, however, introducing GPUs from other vendors, e.g. with the AMD GPU-based OLCF Frontier system just becoming available. […]

This paper presents the Beehive SPIR-V Toolkit; a framework that can automatically generate a Java composable and functional library for dynamically building SPIR-V binary modules. The Beehive SPIR-V Toolkit can be used by optimizing compilers and runtime systems to generate and validate SPIR-V binary modules from managed runtime systems, such as the Java Virtual Machine […]

SYCL began as a C++ abstraction for OpenCL concepts, whereas parallelism in ISO C++ evolved from the algorithms in the standard library. This history has resulted in the two specifications using different terminology to describe parallelism, which is confusing to developers and hinders the SYCL community’s efforts to influence the direction of C++ through experiments […]

Growing heterogeneity and configurability in HPC architectures has made auto-tuning applications and runtime parameters on these systems very complex. Users are presented with a multitude of options to configure parameters. In addition to application specific solutions, a common approach is to use general purpose search strategies, which often might not identify the best configurations or […]

Deep learning (DL) has been a revolutionary technique in various domains. To facilitate the model development and deployment, many deep learning frameworks are proposed, among which PyTorch is one of the most popular solutions. The performance of ecosystem around PyTorch is critically important, which saves the costs of training models and reduces the response time […]

Graphics Processing Units (GPU) offer tremendous computational power by following a throughput oriented computing paradigm where many thousand computational units operate in parallel. Programming this massively parallel hardware is challenging. Programmers must correctly and efficiently coordinate thousands of threads and their accesses to various shared memory spaces. Existing mainstream GPU programming languages, such as CUDA […]

Graphics Processing Units (GPUs) have become an integral part of High-Performance Computing to achieve an Exascale performance. The main goal of application developers of GPU is to tune their code extensively to obtain optimal performance, making efficient use of different resources available. While extracting optimal performance of applications on an HPC infrastructure, developers should also […]

In this thesis, we present KLARAPTOR (Kernel LAunch parameters RAtional Program estimaTOR), a freely available tool to dynamically determine the values of kernel launch parameters of a CUDA kernel. We describe a technique for building a helper program, at the compile-time of a CUDA program, that is used at run-time to determine near-optimal kernel launch […]