high performance computing on graphics processing units: hgpu.org

Deep learning has achieved remarkable success across various domains, ranging from computer vision to healthcare. General-Purpose Graphics Processing Unit (GPGPU) is one of the major driving forces behind this revolution. GPGPUs offer massive parallel computational power, enabling the training and deployment of large-scale neural networks within practical time and resource constraints. Their programmability also enables […]

We describe some features of the A100 memory architecture. In particular, we give a technique to reverse-engineer some hardware layout information. Using this information, we show how to avoid TLB issues to obtain full-speed random HBM access to the entire memory, as long as we constrain any particular thread to a reduced access window of […]

One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular prior is locality, where the atmospheric data is processed with local neural interactions, like 3D convolutions or 3D local attention windows as in Pangu-Weather. On the other hand, some […]

This paper explores practical aspects of using a high-level functional language for GPU-based arithmetic on “midsize” integers. By this we mean integers of up to about a quarter million bits, which is sufficient for most practical purposes. The goal is to understand whether it is possible to support efficient nested-parallel programs with a small, flexible […]

This work presents a performance-oriented study of a heterogeneous application developed with Intel OneAPI to solve two well-known diffusion problems: heat diffusion and image denoising. We have explored CPU+iGPU and CPU+FPGA schemes, applying dynamic load balancing and conducting experiments on Intel DevCloud. The results demonstrate that the CPU+iGPU scheme outperforms the execution times achieved by […]

The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on […]

GPU-based heterogeneous architectures are now commonly used in HPC clusters. Due to their architectural simplicity specialized for data-level parallelism, GPUs can offer much higher computational throughput and memory bandwidth than CPUs in the same generation do. However, as the available resources in GPUs have increased exponentially over the past decades, it has become increasingly difficult […]

Forecasting stock prices remains a considerable challenge in financial markets, bearing significant implications for investors, traders, and financial institutions. Amid the ongoing AI revolution, NVIDIA has emerged as a key player driving innovation across various sectors. Given its prominence, we chose NVIDIA as the subject of our study.

Hardware accelerators have become the backbone of many cloud and HPC workloads, but workloads tend to statically choose accelerators leaving devices unused while others are oversubscribed. We propose a holistic framework that allows a computational kernel to span across multiple devices on a node, as well as multiple applications being scheduled on the same node. […]

Direct numerical simulations (DNS) of turbulence have a virtually unbounded need for computing power. To carry out these simulations, software, computer architectures, and algorithms must operate as efficiently as possible to amortize the large computational cost. However, in a computing landscape increasingly incorporating heterogeneous computer systems, changes are necessary. In this thesis, we consider how […]

As deep learning models become increasingly bigger and more complex, it is critical to improve model training and inference efficiency. Though a variety of highly optimized libraries and packages (known as DL kernels) have been developed, it is tedious and time-consuming to figure out which kernel to use, where to use, and how to use […]

There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications. Equipped with a variety of processing units such as CPUs, GPUs, and NPUs, the mobile devices hold potential to accelerate DL inference via parallel execution across heterogeneous processors. Various efficient parallel methods have been explored […]