high performance computing on graphics processing units: hgpu.org

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, […]

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 […]

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 […]

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 […]

High performance computing is a complex field, with many homogeneous and heterogeneous hardware architectures, and numerous programming paradigms, libraries and compilers. OpenMP and netCDF are relatively widely used in Earth system research because they are comparatively easy to learn and yet can exploit the potential of a single compute node. However, Earth system scientists without […]

Over the past few years, there has been an increased interest in using FPGAs alongside CPUs and GPUs in high-performance computing systems and data centers. This trend has led to a push toward the use of high-level programming models and libraries, such as OpenCL, both to lower the barriers to the adoption of FPGAs by […]

We evaluate the use of the open-source Llama-2 model for generating well-known, high-performance computing kernels (e.g., AXPY, GEMV, GEMM) on different parallel programming models and languages (e.g., C++: OpenMP, OpenMP Offload, OpenACC, CUDA, HIP; Fortran: OpenMP, OpenMP Offload, OpenACC; Python: numpy, Numba, pyCUDA, cuPy; and Julia: Threads, CUDA.jl, AMDGPU.jl). We built upon our previous work […]

In recent history, GPUs became a key driver of compute performance in HPC. With the installation of the Frontier supercomputer, they became the enablers of the Exascale era; further largest-scale installations are in progress (Aurora, El Capitan, JUPITER). But the early-day dominance by NVIDIA and their CUDA programming model has changed: The current HPC GPU […]