high performance computing on graphics processing units: hgpu.org

There is a continuing interest in using standard language constructs for accelerated computing in order to avoid (sometimes vendor-specific) external APIs. For Fortran codes, the {tt do concurrent} (DC) loop has been successfully demonstrated on the NVIDIA platform. However, support for DC on other platforms has taken longer to implement. Recently, Intel has added DC […]

HiCCL (Hierarchical Collective Communication Library) addresses the growing complexity and diversity in high-performance network architectures. As GPU systems have envolved into networks of GPUs with different multilevel communication hierarchies, optimizing each collective function for a specific system has become a challenging task. Consequently, many collective libraries struggle to adapt to different hardware and software, especially […]

We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the efficient parallel computation of input graphs, signals, and processor parameters in GPU. Then, we show its example use under a music mixing scenario, where parameters of every differentiable processor in […]

Web applications have increasingly adopted Deep Learning (DL) through in-browser inference, wherein DL inference performs directly within Web browsers. The actual performance of in-browser inference and its impacts on the quality of experience (QoE) remain unexplored, and urgently require new QoE measurements beyond traditional ones, e.g., mainly focusing on page load time. To bridge this […]

We present HIPRT, an open-source ray tracing framework in HIP. HIPRT provides a versatile, cross-platform solution for professional rendering on contemporary many-core architectures. The core of the framework relies on the bounding volume hierarchy (BVH) with scalable construction algorithms and efficient ray traversal, employing hardware acceleration on AMD GPUs. From a user perspective, we aim […]

Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms and hardware do. Unfortunately, no prior work ever dives deep into the ecosystem of modern DL libraries and provides quantitative results on their performance. In […]

Although specialized hardware promises orders of magnitude performance gains, their uptake has been limited by how challenging it is to program them. Hardware accelerators present challenges programmers are not used to, exposing details of the hardware that are often hidden and requiring new programming styles to use them effectively. Existing programming models often involve learning […]

SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to […]

The computational power of High-Performance Computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At […]

As deep neural networks (DNNs) become increasingly large and complicated, pruning techniques are proposed for lower memory footprint and more efficient inference. The most critical kernel to execute pruned sparse DNNs on GPUs is Sparse-dense Matrix Multiplication (SpMM). To maximize the performance of SpMM, despite the high-performance implementation generated from advanced tensor compilers, they often […]

An increasing number of application domains require high-throughput processing to extract insights from massive data streams. The Data Stream Processing (DSP) paradigm provides formal approaches to analyze structured data streams considered as special, unbounded relations. The most used class of stateful operators in DSP are the ones running sliding-window aggregation, which continuously extracts insights from […]

These lecture notes are designed to accompany an imaginary, virtual, undergraduate, one or two semester course on fundamentals of Parallel Computing as well as to serve as background and reference for graduate courses on High-Performance Computing, parallel algorithms and shared-memory multiprocessor programming. They introduce theoretical concepts and tools for expressing, analyzing and judging parallel algorithms […]