high performance computing on graphics processing units: hgpu.org

The International Conference on Sustainable Development is organized by the European Center of Sustainable Development in collaboration with CIT University. The 6th ICSD 2018 is inspired from the critical challenge of human, environmental, and economic sustainability concerning the present and future generations in a global-scale context. The Conference venue is: Roma Eventi, Congress Center, Piazza […]

Published by: Accepted papers will be published in the conference proceedings, which will be submitted for inclusion into IEEE Xplore, submitted for indexing in EI Compendex and Scopus. The conference proceedings of ICCCS 2015 can be checked in IEEE Xplore Papers of ICCCS 2015 have been indexed by Ei Compendex, and Scopus The conference proceeding […]

In this article we present SkePU 2, the next generation of the SkePU C++ skeleton programming framework for heterogeneous parallel systems. We critically examine the design and limitations of the SkePU 1 programming interface. We present a new, flexible and type-safe, interface for skeleton programming in SkePU 2, and a source-to-source transformation tool which knows […]

Sparse-Matrix Vector products (SpMV) are highly irregular computational kernels that can be found in a diverse collection of high-performance science applications. Performance for this important kernel is often highly correlated with the associated matrix sparsity, which, in turn, governs the computational granularity, and therefore, the efficiency of the memory system. In this paper, we propose […]

In recent years, power consumption has become the main concern in High Performance Computing (HPC). This has lead to heterogeneous computing systems in which Central Processing Units (CPUs) are supported by accelerators, such as Graphics Processing Units (GPUs). While GPUs used to be seen as slave devices to which the main processor offloads computation, today’s […]

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the […]

In recent years, GPUs have been adopted in many High-Performance Computing (HPC) clusters due to their massive computational power and energy efficiency. The Message Passing Interface (MPI) is the de-facto standard for parallel programming. Many HPC applications, written in MPI, use parallel processes and multiple GPUs to achieve higher performance and GPU memory capacity. In […]

Scheduling in High-Performance Computing (HPC) has been traditionally centered around computing resources (e.g., processors/cores). The ever-growing amount of data produced by modern scientific applications start to drive novel architectures and new computing frameworks to support more efficient data processing, transfer and storage for future HPC systems. This trend towards data-driven computing demands the scheduling solutions […]

This work deals with the optimization of computer programs targeting Graphics Processing Units (GPUs). The goal is to lift, from programmers to optimizing compilers, the heavy burden of determining program details that are dependent on the hardware characteristics. The expected benefit is to improve robustness, portability and efficiency of the generated computer programs. We address […]

Going deeper and wider in neural architectures improves the accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. […]

Alchemical free energy calculations (AFE) based on molecular dynamics (MD) simulations are key tools in both improving our understanding of a wide variety of biological processes and accelerating the design and optimization of therapeutics for numerous diseases. Computing power and theory have, however, long been insufficient to enable AFE calculations to be routinely applied in […]

Machine Learning (ML) and Deep Learning (DL) are two technologies used to extract representations of the data for a specific purpose. ML algorithms take a set of data as input to generate one or several predictions. To define the final version of one model, usually there is an initial step devoted to train the algorithm […]