high performance computing on graphics processing units: hgpu.org

With the increasing diversity of heterogeneous architecture in the HPC industry, porting a legacy application to run on different architectures is a tough challenge. In this paper, we present OpenMP Advisor, a first of its kind compiler tool that enables code offloading to a GPU with OpenMP using Machine Learning. Although the tool is currently […]

In this humorous and thought provoking article, we discuss certain myths and legends that are folklore among members of the high-performance computing community. We collected those myths from conversations at conferences and meetings, product advertisements, papers, and other communications such as tweets, blogs, and news articles within (and beyond) our community. We believe they represent […]

Recent trends in language modeling have focused on increasing performance through scaling, and have resulted in an environment where training language models is out of reach for most researchers and practitioners. While most in the community are asking how to push the limits of extreme computation, we ask the opposite question: How far can we […]

In high performance domains like image processing, physics simulation or machine learning, program performance is critical. Programmers called performance engineers are responsible for the challenging task of optimising programs. Two major challenges prevent modern compilers targeting heterogeneous architectures from reliably automating optimisation. First, domain-specific compilers such as Halide for image processing and TVM for machine […]

With the rapid development of deep learning models and hardware support for dense computing, the deep learning (DL) workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broad range of operations scattered across the models. Accelerating a few compute-intensive operations using the expert-tuned implementation of primitives does not fully exploit […]

We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the flexibility needed to handle the requirements of modern autotuning tasks. Particularly, it deals with permutation, ordered, and continuous parameter types along with both known and unknown parameter constraints. To reason about these […]

Today, using multiple heterogeneous accelerators efficiently from applications and high-level frameworks, such as Tensor-Flow and Caffe, poses significant challenges in three respects: (a) sharing accelerators, (b) allocating available resources elastically during application execution, and (c) reducing the required programming effort. In this paper, we present Arax, a runtime system that decouples applications from heterogeneous accelerators […]

Power is increasingly becoming a limiting resource in high-performance, GPU-accelerated computing systems. Understanding the range and sources of power variation is essential in setting realistic bounds on rack and system peak power, and developing techniques that minimize energy. While variations arising during manufacturing and other factors like algorithm among others have been previously studied, this […]

As the architectures of super-computing systems are continually changing, it is important to maintain efficient code portability in order to continue to take advantage of the computing capabilities of the diverse and evolving hardware in these systems. Intel has adopted an open standard programming interface for heterogeneous systems called oneAPI, designed to allow code portability […]

High-level synthesis compilers (HLS) enable the rapid creation of accelerator circuits. Unfortunately, compiler generated RTL (H-RTL) is inconsistent in terms of quality, hard to comprehend, and tends to be brittle [28, 41]. This paper develops a framework to help HLS compiler architects inspect and profile H-RTL. Prior state-of-the-art tools [23, 57] have predominantly focused on […]

Fine-grained workload and resource balancing is the key to high performance for regular and irregular computations on the GPUs. In this dissertation, we conduct an extensive survey of existing load-balancing techniques to build an abstraction that addresses the difficulty of scheduling computations on the GPU. We propose a GPU fine-grained load-balancing abstraction that decouples load […]

Serverless computing has made it easier than ever to deploy applications over scalable cloud resources, all the while driving higher utilization for cloud providers. While this technique has worked well for easily divisible resources like CPU and local DRAM, it has struggled to incorporate more expensive and monolithic resources like GPUs or other application accelerators. […]