high performance computing on graphics processing units: hgpu.org

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. […]

Agent-Based Simulation (ABS) is a modelling approach where simulated entities i.e., agents, perform actions autonomously and interact with other agents based on a set of rules. ABSs have demonstrated their usefulness in various domains such as transportation, social science, or biology. Agent-based simulators commonly rely vastly on Central Processing Unit (CPU)-based sequential execution. As a […]

Programming in low-level imperative languages provides good performance but is error-prone. In contrast, high-level functional programming is usually free from low-level errors but performance suffers from costly abstractions. To benefit from both worlds, approaches like Lift compile from high-level functional programs to high-performance imperative code. However, problems such as removing high-level abstraction costs and handling […]

There has been significant progress in developing neural network architectures that both achieve high predictive performance and that also achieve high application-level inference throughput (e.g., frames per second). Another metric of increasing importance is GPU utilization during inference: the measurement of how well a deployed neural network uses the computational capabilities of the GPU on […]

Accelerators, such as GPUs (Graphics Processing Unit) that is suitable for handling highly parallel data, and FPGA (Field Programmable Gate Array) with algorithms customized architectures, are widely adopted. The motivation is that algorithms with various parallel characteristics can efficiently map to the heterogeneous computing architecture by collaborated GPU and FPGA. However, current applications always utilize […]

This paper introduces the Yet Another Kernel Launcher (YAKL) C++ portability library, which strives to enable user-level code with the look and feel of Fortran code. The intended audience includes both C++ developers and Fortran developers unfamiliar with C++. The C++ portability approach is briefly explained, YAKL’s main features are described, and code examples are […]

We analyze the performance portability of the skeleton-based, single-source multi-backend high-level programming framework SkePU across multiple different CPU–GPU heterogeneous systems. Thereby, we provide a systematic application efficiency characterization of SkePU-generated code in comparison to equivalent hand-written code in more low-level parallel programming models such as OpenMP and CUDA. For this purpose, we contribute ports of […]

With the rise of AI in recent years and the increase in complexity of the models, the growing demand in computational resources is starting to pose a significant challenge. The need for higher compute power is being met with increasingly more potent accelerators and the use of large compute clusters. However, the gain in prediction […]

Our goal in this work is to make it easy and feasible to implement solutions for autonomous decision-making and planning under uncertainty on low-power mobile platforms. We focus on practical applications, such as autonomous driving and service robotics, that must run on mobile SoC platforms. These applications often have real-time execution constraints. The main challenge […]

The most compute-intensive stage of deep neural network (DNN) training is matrix multiplication where the multiply-accumulate (MAC) operator is key. To reduce training costs, we consider using low-precision arithmetic for MAC operations. While low-precision training has been investigated in prior work, the focus has been on reducing the number of bits in weights or activations […]