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Posts

Feb, 6

GPU Hackathons, 2016

Background General-purpose Graphics Processing Units (GPGPUs) potentially offer exceptionally high memory bandwidth and performance for a wide range of applications. The challenge in utilizing such accelerators has been the difficulty in programming them. The OpenACC Directives for Accelerators offers straightforward pragma extensions to C++ and Fortran to address this programming hurdle, but other GPU programming […]
Feb, 6

FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only

Deep neural networks (DNNs) demand a very large amount of computation and weight storage, and thus efficient implementation using special purpose hardware is highly desired. In this work, we have developed an FPGA based fixed-point DNN system using only on-chip memory not to access external DRAM. The execution time and energy consumption of the developed […]
Feb, 6

Asynchronous Methods for Deep Reinforcement Learning

We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. […]
Feb, 6

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

This paper focuses on evaluating the impact of different data layouts on the computational efficiency of GPU-accelerated Inverse Distance Weighting (IDW) interpolation algorithm. First we redesign and improve our previous GPU implementation that was performed by exploiting the feature of CUDA dynamic parallelism (CDP). Then we implement three versions of GPU implementations, i.e., the naive […]
Feb, 6

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

In this paper we present PRISM-PSY, a novel tool that performs precise GPU-accelerated parameter synthesis for continuous-time Markov chains and time-bounded temporal logic specifications. We redesign, in terms of matrix-vector operations, the recently formulated algorithms for precise parameter synthesis in order to enable effective dataparallel processing, which results in significant acceleration on many-core architectures. High […]
Feb, 6

EIE: Efficient Inference Engine on Compressed Deep Neural Network

State-of-the art deep neural networks (DNNs) have hundreds of millions of connections and are both computationally and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources and power budgets. While custom hardware can help the computation, fetching the weights from DRAM can be as much as two orders of magnitude […]
Feb, 4

A Performance Analysis Framework for Optimizing OpenCL Applications on FPGAs

Recently, FPGA vendors such as Altera and Xilinx have released OpenCL SDK for programming FPGAs. However, the architecture of FPGA is significantly different from that of CPU/GPU, for which OpenCL is originally designed. Tuning the OpenCL code for good performance on FPGAs is still an open problem, since the existing OpenCL tools and models designed […]
Feb, 4

Workshop on Exascale Multi/Many Core Computing Systems, 2016

* CONTEXT Exascale computing will revolutionize computational science and engineering by providing 1000x the capabilities of currently available computing systems, while having a similar power footprint. The HPC community is working towards the development of the first Exaflop computer after reaching the Petaflop milestone in 2008. There are concerns that computer designs based on existing […]
Feb, 3

Optimization and Large Scale Computation of an Entropy-Based Moment Closure

We present computational advances and results in the implementation of an entropy-based moment closure, M_N, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as P_N, but the […]
Feb, 3

Deep Learning For Smile Recognition

Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features […]
Feb, 3

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Compact finite difference schemes are widely used in the direct numerical simulation of fluid flows for their ability to better resolve the small scales of turbulence. However, they can be expensive to evaluate and difficult to parallelize. In this work, we present an approach for the computation of compact finite differences and similar tridiagonal schemes […]
Feb, 3

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

Betweenness Centrality (BC) is steadily growing in popularity as a metrics of the influence of a vertex in a graph. The BC score of a vertex is proportional to the number of all-pairs-shortest-paths passing through it. However, complete and exact BC computation for a large-scale graph is an extraordinary challenge that requires high performance computing […]
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