high performance computing on graphics processing units: hgpu.org

To facilitate efficient embedded and hardware implementations of deep neural networks (DNNs), two important categories of DNN model compression techniques: weight pruning and weight quantization are investigated. The former leverages the redundancy in the number of weights, whereas the latter leverages the redundancy in bit representation of weights. However, there lacks a systematic framework of […]

There is a large body of legacy scientific code written in languages like Fortran that is not optimised to get the best performance out of heterogeneous acceleration devices like GPUs and FPGAs, and manually porting such code into parallel languages frameworks like OpenCL requires considerable effort. We are working towards developing a turn-key, self-optimising compiler […]

Design of hardware accelerators for neural network (NN) applications involves walking a tight rope amidst the constraints of low-power, high accuracy and throughput. NVIDIA’s Jetson is a promising platform for embedded machine learning which seeks to achieve a balance between the above objectives. In this paper, we provide a survey of works that evaluate and […]

Emerging computer architectures and advanced computing technologies, such as Intel’s Many Integrated Core (MIC) Architecture and graphics processing units (GPU), provide a promising solution to employ parallelism for achieving high performance, scalability and low power consumption. As a result, accelerators have become a crucial part in developing supercomputers. Accelerators usually equip with different types of […]

Data analytics is a process of examining datasets using various analytical and statistical techniques. Several tools have been proposed in the literature to extract hidden patterns, gather insights and build mathematical models from large datasets. However, these tools have been known to be computationally demanding as the datasets become larger over time. Two such recently […]

We investigate the problem of exact set similarity joins using a co-process CPU-GPU scheme. The state-of-the-art CPU solutions split the wok in two main phases. First, filtering and index building takes place to reduce the candidate sets to be compared as much as possible; then the pairs are compared to verify whether they should become […]

This paper proposes an efficient neural network (NN) architecture design methodology called Chameleon that honors given resource constraints. Instead of developing new building blocks or using computationally-intensive reinforcement learning algorithms, our approach leverages existing efficient network building blocks and focuses on exploiting hardware traits and adapting computation resources to fit target latency and/or energy constraints. […]

IWOCL is the annual gathering of international community of OpenCL, SYCL and SPIR developers, researchers, suppliers and members of the Khronos Working Groups to share best practise, and to promote the evolution and advancement of the standard. The meeting is open to anyone who is interested in contributing to and participating in the community and […]

This will be the 3rd DHPCC++ event in partnership with IWOCL, the international OpenCL workshop with a focus on heterogeneous programming models for C and C++, covering all the programming models that have been designed to support heterogeneous programming in C and C++. Many C++ programming models exist including SYCL, HPX, KoKKos, Raja, C++AMP, HCC, […]

This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. Here we explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than […]

Over the past two decades, High-Performance Computing (HPC) communities have developed many models for delivering education aiming to help students understand and harness the power of parallel and distributed computing. Most of these courses either lack a hands-on component or heavily focus on theoretical characterization behind complex algorithms. To bridge the gap between application and […]

Simulation has become pervasive in science. Real experimentation remains an essential step in scientific research, but simulation replaced a wide range of costly and lengthy or even dangerous experimentation. It however requires massive computation power, and scientists will always welcome bigger and faster computation platforms, to be able to keep simulating more and more accurately […]