high performance computing on graphics processing units: hgpu.org

The pervasive adoption of Deep Learning (DL) and Graph Processing (GP) makes it a de facto requirement to build large-scale clusters of heterogeneous accelerators including GPUs and FPGAs. The OpenCL programming framework can be used on the individual nodes of such clusters but is not intended for deployment in a distributed manner. Fortunately, the original […]

Energy optimization is an increasingly important aspect of today’s high-performance computing applications. In particular, dynamic voltage and frequency scaling (DVFS) has become a widely adopted solution to balance performance and energy consumption, and hardware vendors provide management libraries that allow the programmer to change both memory and core frequencies manually to minimize energy consumption while […]

Recent years have seen an increase of heterogeneous architectures combining multi-core CPUs with accelerators such as GPU, FPGA, and Intel Xeon Phi. GPU can achieve significant performance for certain categories of application. Nevertheless, achieving this performance with low-level APIs (e.g. CUDA, OpenCL) requires to rewrite the sequential code, to have a good knowledge of GPU […]

Tensor computation plays a paramount role in a broad range of domains, including machine learning, data analytics, and scientific computing. The wide adoption of tensor computation and its huge computation cost has led to high demand for flexible, portable, and high-performance library implementation on heterogeneous hardware accelerators such as GPUs and FPGAs. However, the current […]

Constructing compilers is hard. Optimising compilers are multi-million dollar projects spanning years of development, yet remain unable to fully exploit the available performance, and are prone to bugs. The rapid transition to heterogeneous parallelism and diverse architectures has raised demand for aggressively-optimising compilers to an all time high, leaving compiler developers struggling to keep up. […]

Today, many big data applications require massively parallel tasks to compute complicated mathematical operations. To perform parallel tasks, platforms like CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language) are widely used and developed to enhance the throughput of massively parallel tasks. There is also a need for high-level abstractions and platform-independence over those […]

High-performance computing developers are faced with the challenge of optimizing the performance of OpenCL workloads on diverse architectures. The Architecture-Independent Workload Characterization (AIWC) tool is a plugin for the Oclgrind OpenCL simulator that gathers metrics of OpenCL programs that can be used to understand and predict program performance on an arbitrary given hardware architecture. However, […]

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 […]

This paper presents EASYPAP, an easy-to-use programming environment designed to help students to learn parallel programming. EASYPAP features a wide range of 2D computation kernels that the students are invited to parallelize using Pthreads, OpenMP, OpenCL or MPI. Execution of kernels can be interactively visualized, and powerful monitoring tools allow students to observe both the […]

Optimizing programs to run efficiently on modern parallel hardware is hard but crucial for many applications. The predominantly used imperative languages – like C or OpenCL – force the programmer to intertwine the code describing functionality and optimizations. This results in a nightmare for portability which is particularly problematic given the accelerating trend towards specialized […]

Due to the ever-increasing computational demand of automotive applications, and in particular autonomous driving functionalities, the automotive industry and supply vendors are starting to adopt parallel and heterogeneous embedded platforms for their products. However, C and C++, the currently dominating programming languages in this industry, do not provide sufficient mechanisms to target such platforms. Established […]

The advent of modern cloud services along with the huge volume of data produced on a daily basis, have set the demand for fast and efficient data processing. This demand is common among numerous application domains, such as deep learning, data mining, and computer vision. Prior research has focused on employing hardware accelerators as a […]