high performance computing on graphics processing units: hgpu.org

For computational fluid dynamics (CFD) applications with a large number of grid points/cells, parallel computing is a common efficient strategy to reduce the computational time. How to achieve the best performance in the modern supercomputer system, especially with heterogeneous computing resources such as hybrid CPU+GPU, or a CPU + Intel Xeon Phi (MIC) co-processors, is […]

This PhD project is motivated by the need to develop and achieve better and energy efficient computing through the use of parallelism and heterogeneous systems. Our contribution consists of both theoretical aspects, as well as in-depth and comprehensive empirical studies that aim to provide more insight into parallel and heterogeneous computing. Our first problem is […]

We present early results of the deep learning work on the Stampede2 supercomputer. Our goal is to enable scalable and efficient deep learning model training and serving to expedite scientific discovery. We build three popular deep learning frameworks, namely, IntelCaffe, MXNet, and TensorFlow. With the built-in applications of these frameworks (CaffeNet, AlexNet, GoogLeNet, and Cifar10), […]

In this work we use the GPU porting task for the operative Japanese weather prediction model "ASUCA" as an opportunity to examine productivity issues with OpenACC when applied to structured grid problems. We then propose "Hybrid Fortran", an approach that combines the advantages of directive based methods (no rewrite of existing code necessary) with that […]

The GooFit package provides physicists a simple, familiar syntax for manipulating probability density functions and performing fits, and is highly optimized for data analysis on NVIDIA GPUs and multithreaded CPU backends. GooFit was updated to version 2.0, bringing a host of new features. A completely revamped and redesigned build system makes GooFit easier to install, […]

Graphics Processing Units (GPUs) and other parallel devices are widely available and have the potential for accelerating a wide class of algorithms. However, expert programming skills are required to achieving maximum performance. hese devices expose low-level hardware details through imperative programming interfaces where programmers explicity encode device-specific optimisation strategies. This inevitably results in non-performance-portable programs […]

The increasing attention on deep learning has tremendously spurred the design of intelligence processing hardware. The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware). However, existing benchmarks are unsuitable for benchmarking intelligence processors due to their non-diversity and nonrepresentativeness. Also, the lack of a […]

We present Deep Voice 3, a fully-convolutional attention-based neural text-to-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training ten times faster. We scale Deep Voice 3 to data set sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In […]

Reduction operations are extensively employed in many computational problems. A reduction consists of, given a finite set of numeric elements, combining into a single value all elements in that set, using for this a combiner function. A parallel reduction, in turn, is the reduction operation concurrently performed when multiple execution units are available. The current […]

In this paper, we propose a High performance Parallel Computing method for the Inverse of a symmetric positive definite (SPD) matrix. Brought in the reuse of the inverse of diagonal sub blocks technique and Combined with the newest OpenCL parallel computing framework, this methods can improve computing the inverse of SPD matrix effectively. Computing the […]

Elegant is an accelerator physics and particle-beam dynamics code widely used for modeling and design of a variety of high-energy particle accelerators and accelerator-based systems. In this paper we discuss a recently developed version of the code that can take advantage of CUDA-enabled graphics processing units (GPUs) to achieve significantly improved performance for a large […]

With increase in GPU register file (RF) size, its power consumption has also increased. Since RF exists at the highest level in cache hierarchy, designing it with memories with high write latency/energy (e.g., spin transfer torque RAM) can lead to large energy loss. In this paper, we present an spin orbit torque RAM (SOT-RAM) based […]