high performance computing on graphics processing units: hgpu.org

As supercomputers become larger with powerful Graphics Processing Unit (GPU), traditional direct eigensolvers struggle to keep up with the hardware evolution and scale efficiently due to communication and synchronization demands. Conversely, subspace eigensolvers, like the Chebyshev Accelerated Subspace Eigensolver (ChASE), have a simpler structure and can overcome communication and synchronization bottlenecks. ChASE is a modern […]

High-Level Synthesis (HLS) tools are mature enough to provide efficient code generation for computation kernels on FPGA hardware. For more complex applications, multiple kernels may be connected by a dataflow graph. Although some tools, such as Xilinx Vitis HLS, support dataflow directives, they lack efficient analysis methods to compute the buffer sizes between kernels in […]

Limited resource hardware devices are more affordable and energy efficient than high-end hardware. Despite their reduced size, these devices are increasingly complex, with many now featuring multiple processing cores, GPGPU accelerators, and larger RAM capacity. To fully utilize their computational capacity, software developers must exploit parallelism, but this adds an extra layer of complexity because […]

Traditional optimization methods rely on the use of single-precision floating point arithmetic, which can be costly in terms of memory size and computing power. However, mixed precision optimization techniques leverage the use of both single and half-precision floating point arithmetic to reduce memory requirements while maintaining model accuracy. We provide here an algorithm to further […]

Cas-OFFinder is a popular application written in OpenCL for searching potential off-target sites in parallel on a GPU. In this work, we describe our experience of migrating the application from OpenCL to SYCL. Evaluating the performance of the OpenCL and SYCL application using human genome sequences shows that the SYCL program could achieve performance portability […]

In contemporary high-performance computing architectures, the integration of GPU accelerators has become increasingly prevalent. To harness the full potential of these accelerators, developers often resort to vendor-specific kernel languages, such as CUDA. While this approach ensures optimal efficiency, it inherently compromises portability and engenders vendor dependency. Existing portable programming models, such as OpenMP, while promising, […]

Deep Learning has achieved state-of-the-art performance in several artificial intelligence tasks like object recognition, speech recognition, machine translation, and summarization. Deep learning is a subset of machine learning that learns multiple levels of data representation using Neural Networks (NNs). The rise of deep learning can be attributed to the presence of large datasets and computation […]

The Standard Portable Intermediate Representation (SPIR-V) is a low-level binary format designed for representing shaders and compute kernels that can be consumed by OpenCL for computing kernels, and Vulkan for graphics rendering. As a binary representation, SPIR-V is meant to be used by compilers and runtime systems, and is usually performed by C/C++ programs and […]

We evaluate using Julia as a single language and ecosystem paradigm powered by LLVM to develop workflow components for high-performance computing. We run a Gray-Scott, 2-variable diffusion-reaction application using a memory-bound, 7-point stencil kernel on Frontier, the US Department of Energy’s first exascale supercomputer. We evaluate the feasibility, performance, scaling, and trade-offs of (i) the […]

In this paper, we evaluate the portability of the SYCL programming model on some of the latest CPUs and GPUs from a wide range of vendors, utilizing the two main compilers: DPC++ and hipSYCL/OpenSYCL. Both compilers currently support GPUs from all three major vendors; we evaluate performance on the Intel(R) Data Center GPU Max 1100, […]

The heterogeneous computing paradigm has led to the need for portable and efficient programming solutions that can leverage the capabilities of various hardware devices, such as NVIDIA, Intel, and AMD GPUs. This study evaluates the portability and performance of the SYCL and CUDA languages for one fundamental bioinformatics application (Smith-Waterman protein database search) across different […]

As recently demonstrated, Deep Neural Networks (DNN), usually trained using single precision IEEE 754 floating point numbers (binary32), can also work using lower precision. Therefore, 16-bit and 8-bit compressed format have attracted considerable attention. In this paper, we focused on two families of formats that have already achieved interesting results in compressing binary32 numbers in […]