high performance computing on graphics processing units: hgpu.org

In this paper, we focus on three sparse matrix operations that are relevant for machine learning applications, namely, the sparse-dense matrix multiplication (SPMM), the sampled dense-dense matrix multiplication (SDDMM), and the composition of the SDDMM with SPMM, also termed as FusedMM. We develop optimized implementations for SPMM, SDDMM, and FusedMM operations utilizing Intel oneAPI’s Explicit […]

With the serial performance of CPUs improving exponentially through the 1980s and 1990s and then plateauing by the mid-2000s, the high-performance computing community has seen parallel computing become ubiquitous, which, in turn, has led to a proliferation of parallel programming models. This diversity in hardware platform and programming model has forced programmers to port their […]

We introduce OpenRAND, a C++17 library aimed at facilitating reproducible scientific research through the generation of statistically robust and yet replicable random numbers. OpenRAND accommodates single and multi-threaded applications on CPUs and GPUs and offers a simplified, user-friendly API that complies with the C++ standard’s random number engine interface. It is portable: it functions seamlessly […]

GPU-embedded systems have gained popularity across various domains due to their efficient power consumption. However, in order to meet the demands of real-time or time-consuming applications running on these systems, it is crucial for them to be tuned to exhibit high performance. This paper addresses the issue by developing and comparing two tuning methodologies on […]

In this new era where multiple GPU vendors are leading the supercomputing landscape, and multiple programming models are available to users, the drive to achieve performance portability across platforms faces new challenges. Consider stencil algorithms, where architecture-specific solutions are required to optimize for the parallelism hierarchy and memory hierarchy of emerging systems. In this work, […]

Sparse matrix-vector product (SpMV) is a central operation in many iterative methods for solving linear systems and as such is an attractive candidate for acceleration on the GPU. However, the performance of the SpMV kernel can vary depending both on the target architecture as well as on the sparsity pattern of the matrix. Thus, to […]

The first generation of exascale systems will include a variety of machine architectures, featuring GPUs from multiple vendors. As a result, many developers are interested in adopting portable programming models to avoid maintaining multiple versions of their code. It is necessary to document experiences with such programming models to assist developers in understanding the advantages […]

Parallel accelerators, such as GPUs, are key enablers for large-scale Machine Learning (ML) applications. However, ML model developers often lack detailed knowledge of the underlying system architectures, while system programmers usually do not have a high-level understanding of the ML model that runs on the specific system. To mitigate this gap between two relevant aspects […]

The rise of power-efficient embedded computers based on highly-parallel accelerators opens a number of opportunities and challenges for researchers and engineers, and paved the way to the era of edge computing. At the same time, advances in embedded AI for object detection and categorization such as YOLO, GoogleNet and AlexNet reached an unprecedented level of […]

In this work, the capability of SYCL™ to execute code on different hardware devices is investigated. This motivates conducting a performance portability analysis. The architectures investigated are the CPU, GPU, and FPGA. As a benchmark algorithm, the CG algorithm is used, as it is widely applicable to many fields and is more complex than simple […]

During the last couple of decades, there has been an exponential growth in the amount of time and energy required to run workloads on high-performance computing systems, which nowadays rely heavily upon GPUs. In order to reduce the resources required by these systems, one clear approach is to avoid inefficient applications by using prediction models […]

Edge computing is essential to handle increasing data volumes and processing capacities. It provides real-time, secure data processing near data sources, like smart devices, alleviating cloud computing energy use and saving network bandwidth. Specialized accelerators, like GPUs and FPGAs, are vital for low-latency edge computing but the requirements to customized code for different hardware and […]