high performance computing on graphics processing units: hgpu.org

The application of accelerators in HPC applications has seen enormous growth in the last decade. In the field of HPC demands on throughput are steadily growing. Not all of the algorithms used have a clear HW architecture which performs the best. Our work explores the performance of different HW architectures in solving a convex optimization […]

Asynchronous many-task (AMT) runtimes are maturing as a model for computing simulations on a diverse range of architectures at large-scale. The Uintah AMT framework is driven by a philosophy of maintaining an application layer distinct from the underlying runtime while operating on an adaptive mesh grid. This model has enabled task developers to focus on […]

How to discover and evaluate the true strength of models quickly and accurately is one of the key challenges in Neural Architecture Search (NAS). To cope with this problem, we propose an Architecture-Driven Weight Prediction (ADWP) approach for neural architecture search (NAS). In our approach, we first design an architecture-intensive search space and then train […]

In this paper, we propose a novel method, GSM (Gunrock Subgraph Matching), to compute graph matching (subgraph isomorphism) on GPUs. In contrast to previous approaches, GSM is BFS-based: possible matches are explored simultaneously in a breadth-first strategy and thus can be mapped onto GPUs in a massively parallel fashion. Our implementation on the Gunrock graph […]

A novel inline data compression method is presented for single-precision vectors in three dimensions. The primary application of the method is for accelerating computational physics calculations where the throughput is bound by memory bandwidth. The scheme employs spherical polar coordinates, angle quantisation, and a bespoke floating-point representation of the magnitude to achieve a fixed compression […]

Applications are migrating en masse to the cloud, while accelerators such as GPUs, TPUs, and FPGAs proliferate in the wake of Moore’s Law. These trends are in conflict: cloud applications run on virtual platforms, but existing virtualization techniques have not provided production-ready solutions for accelerators. As a result, cloud providers expose accelerators by dedicating physical […]

Convolutional Neural Network is a deep learning algorithm that brings revolutionary impact on computer vision area. One of its applications is image classification. However, problem exists in this algorithm that it involves huge number of operations and parameters, which limits its possibility in time and resource restricted embedded applications. MobileNet, a neural network that uses […]

GPUs have become ubiquitous in the cloud due to the dramatic performance gains they enable in domains such as machine learning and computer vision. However, offloading GPU computation to the cloud requires placing enormous trust in providers and administrators. Recent proposals for GPU trusted execution environments (TEEs) are promising but fail to address very real […]

AUTODOCK is a molecular docking application that consists of a genetic algorithm coupled with the Solis-Wets localsearch method. Despite its wide usage, its power consumption on heterogeneous systems has not been evaluated extensively. In this work, we evaluate the energy efficiency of an OpenCL-accelerated version of AUTODOCK that, along with the traditional SolisWets method, newly […]

SpGEMM (General Sparse Matrix-Matrix Multiplication) has attracted much attention from researchers in fields of multigrid methods and graph analysis. Many optimization techniques have been developed for certain application fields and computing architecture over the decades. The objective of this paper is to provide a structured and comprehensive overview of the research on SpGEMM. Existing optimization […]

GPUs have recently become important computational units on mobile devices, resulting in heterogeneous devices that can run a variety of parallel processing applications. While developing and optimizing such applications, estimating power consumption is of immense importance as energy efficiency has become the key design constraint to optimize for on these platforms. In this work, we […]

Graphics processor units (GPUs) are prevalent in modern computing systems at all scales. They consume a significant fraction of the energy in these systems. However, vendors do not publish the actual cost of the power/energy overhead of their internal microarchitecture. In this paper, we accurately measure the energy consumption of various instructions found in modern […]