high performance computing on graphics processing units: hgpu.org

The purpose of this thesis is to determine the performance of convolutional neural networks in classifications per millisecond, not training or accuracy, for the GTX960 and the TegraX1. This is done through varying parameters of the convolutional neural networks and using the Python framework Theano’s function profiler to measure the time taken for different networks. […]

Recurrent neural networks (RNNs) have achieved state-of-the-art performances in many natural language processing tasks, such as language modeling and machine translation. However, when the vocabulary is large, the RNN model will become very big (e.g., possibly beyond the memory capacity of a GPU device) and its training will become very inefficient. In this work, we […]

In this paper, we present a data decomposition method for multi-dimensional data, aiming at realizing multi graphics processing unit (GPU) acceleration of a compute unified device architecture (CUDA) code written for a single GPU. Our multi-dimensional method extends a previous method that deals with one-dimensional (1-D) data. The method performs a sample run of selected […]

Specialized image processing accelerators are necessary to deliver the performance and energy efficiency required by important applications in computer vision, computational photography, and augmented reality. But creating, "programming,"and integrating this hardware into a hardware/software system is difficult. We address this problem by extending the image processing language, Halide, so users can specify which portions of […]

Lattice Boltzmann Method (LBM) is a powerful numerical simulation method of the fluid flow. With its data parallel nature, it is a promising candidate for a parallel implementation on a GPU. The LBM, however, is heavily dataintensive and memory bound. In particular, moving the data to the adjacent cells in the streaming computation phase incurs […]

Modern graphics processing units (GPUs) are using increasingly larger register file (RF) which occupies a large fraction of GPU core area and is very frequently accessed. This makes RF vulnerable to soft-errors (SE). In this paper, we present two techniques for improving SE resilience of GPU RF. First, we propose compressing the RF values for […]

Call for Papers The 2nd International SYCL Workshop (SYCL 2017) Held in conjunction with ACM PPoPP 2017, Austin, Texas – February 4-7, 2017 https://codeplaysoftware.github.io/sycl-ppopp2017 SYCL (sɪkəl – as in sickle) is a royalty-free, cross-platform Khronos specification facilitating a C++ abstraction layer that builds on the underlying concepts, portability and efficiency of OpenCL, while adding the […]

A new multi-threaded variant of the quicksort algorithm called AQsort and its C++/OpenMP implementation are presented. AQsort operates in place and was primarily designed for high-performance computing (HPC) runtime environments. It can work with multiple arrays at once; such a functionality is frequently required in HPC and cannot be accomplished with standard C pointer-based or […]

Graphics Processing Units (GPUs) can easily outperform CPUs in processing large-scale data parallel workloads, but are considered weak in processing serialized tasks and communicating with other devices. Pursuing a CPU-GPU collaborative computing model which takes advantage of both devices could provide an important breakthrough in realizing the full performance potential of heterogeneous computing. In recent […]

GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and […]

The last decade has witnessed the blooming emergence of general-purpose Graphic-Processing-Unit computing (GPGPU). With the exponential growth of cores and threads in a modern GPU processor, how to analyze and optimize its performance becomes a grand challenge. In this thesis, as the modeling part, we propose an analytic model for throughput-oriented parallel processors. The model […]

The tree method is a widely implemented algorithm for collisionless N-body simulations in astrophysics well suited for GPU(s). Adopting hierarchical time stepping can accelerate N-body simulations; however, it is infrequently implemented and its potential remains untested in GPU implementations. We have developed a Gravitational Oct-Tree code accelerated by HIerarchical time step Controlling named GOTHIC, which […]