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Posts

Dec, 30

Automatic Performance Optimization on Heterogeneous Computer Systems using Manycore Coprocessors

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 […]
Dec, 30

A Study on the Acceleration of Arrival Curve Construction and Regular Specification Mining using GPUs

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 […]
Dec, 30

Speeding-up the Verification Phase of Set Similarity Joins in the GPGPU paradigm

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 […]
Dec, 30

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

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. […]
Dec, 29

7th International Workshop on OpenCL, 2019

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 […]
Dec, 29

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

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, […]
Dec, 23

wav2letter++: The Fastest Open-source Speech Recognition System

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 […]
Dec, 23

Deep Learning by Doing: The NVIDIA Deep Learning Institute and University Ambassador Program

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 […]
Dec, 23

On Runtime Systems for Task-based Programming on Heterogeneous Platforms

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 […]
Dec, 23

Targeting GPUs with OpenMP Directives on Summit: A Simple and Effective Fortran Experience

We use OpenMP directives to target hardware accelerators (GPUs) on Summit, a newly deployed supercomputer at the Oak Ridge Leadership Computing Facility (OLCF), demonstrating simplified access to GPU devices for users of our astrophysics code GenASiS and useful speedup on a sample fluid dynamics problem. At a lower level, we use the capabilities of Fortran […]
Dec, 23

cuPC: CUDA-based Parallel PC Algorithm for Causal Structure Learning on GPU

The main goal in many fields in empirical sciences is to discover causal relationships among a set of variables from observational data. PC algorithm is one of the promising solutions to learn the underlying causal structure by performing a number of conditional independence tests. In this paper, we propose a novel GPU-based parallel algorithm, called […]
Dec, 16

Software Platform for Hybrid Resource Management of Many-core Accelerators

The ever-increasing computational demand from workload mix of concurrent applications characterizes modern embedded systems. In response to such a trend, many-core accelerators are becoming more popular in high-end embedded systems. However, embedded systems usually have many constraints compared to general purpose computers. Various constraints such as low computing powers, lack of operating system and restriction […]

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