high performance computing on graphics processing units: hgpu.org

Deep learning kernels exhibit predictable memory accesses and compute patterns, making GPUs’ parallel architecture well-suited for their execution. Software and runtime systems for GPUs are optimized to better utilize the stream multiprocessors, on-chip cache, and off-chip high-bandwidth memory. As deep learning models and GPUs evolve, access to newer GPUs is often limited, raising questions about […]

Deep learning (DL) has demonstrated significant success across diverse fields, leading to the construction of dedicated GPU accelerators within GPU clusters for high-quality training services. Efficient scheduler designs for such clusters are vital to reduce operational costs and enhance resource utilization. While recent schedulers have shown impressive performance in optimizing DL job performance and cluster […]

Data-intensive applications have attracted considerable attention from researchersin information sciences and enterprises, as these applications have made evolutionary breakthroughs in scientific fields and are extremely valuable to produce productivity in businesses. Recently, as the high speed growth of the new generated data, researchers have begun to leverage the useful knowledge hidden in such huge volume […]

This paper compares OpenCL, CUDA, and HIP as compilation targets for Futhark, a functional array language. We compare the performance of OpenCL versus CUDA, and OpenCL versus HIP, on the code generated by the Futhark compiler on a collection of 48 application benchmarks on two different GPUs. Despite the generated code in most cases being […]

Many studies have focused on developing and improving auto-tuning algorithms for Nvidia Graphics Processing Units (GPUs), but the effectiveness and efficiency of these approaches on AMD devices have hardly been studied. This paper aims to address this gap by introducing an auto-tuner for AMD’s HIP. We do so by extending Kernel Tuner, an open-source Python […]

Nowadays, Large Language Models (LLMs) have been trained using extended context lengths to foster more creative applications. However, long context training poses great challenges considering the constraint of GPU memory. It not only leads to substantial activation memory consumption during training, but also incurs considerable memory fragmentation. To facilitate long context training, existing frameworks have […]

This paper introduces a random-batch molecular dynamics (RBMD) package for fast simulations of particle systems at the nano/micro scale. Different from existing packages, the RBMD uses random batch methods for nonbonded interactions of particle systems. The long-range part of Coulomb interactions is calculated in Fourier space by the random batch Ewald algorithm, which achieves linear […]

Sparse matrix-vector multiplication (SpMV) is one of the important kernels of many iterative algorithms for solving sparse linear systems. The limited storage and computational resources of individual GPUs restrict both the scale and speed of SpMV computing in problem-solving. As real-world engineering problems continue to increase in complexity, the imperative for collaborative execution of iterative […]

Memory management across discrete CPU and GPU physical memory is traditionally achieved through explicit GPU allocations and data copy or unified virtual memory. The Grace Hopper Superchip, for the first time, supports an integrated CPU-GPU system page table, hardware-level addressing of system allocated memory, and cache-coherent NVLink-C2C interconnect, bringing an alternative solution for enabling a […]

Scientific computing is an amalgamation of numerical methods and computer science. Developments in numerical analysis have allowed stable and accurate numerical schemes, whereas computer algorithms have been successfully adopted to standard multicore systems of today, enabling parallelism. Combining efficient numerical algorithms with efficient parallelism presents a challenge mainly due to the independent development of these […]

As home network-based devices and servers become more accessible [1], the need for cybersecurity awareness and best practices to secure wireless network is increasingly important. With the growing affordability of advanced hardware technology, such as modern gaming PCs equipped with powerful graphics processing units (GPUs), which can facilitate password brute force cracking on a wider […]

In High-Level Synthesis (HLS), converting a regular C/C++ program into its HLS-compatible counterpart (HLS-C) still requires tremendous manual effort. Various program scripts have been introduced to automate this process. But the resulting codes usually contain many issues that should be manually repaired by developers. Since Large Language Models (LLMs) have the ability to automate code […]