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Posts

Nov, 3

MILC staggered conjugate gradient performance on Intel KNL

We review our work done to optimize the staggered conjugate gradient (CG) algorithm in the MILC code for use with the Intel Knights Landing (KNL) architecture. KNL is the second generation Intel Xeon Phi processor. It is capable of massive thread parallelism, data parallelism, and high on-board memory bandwidth and is being adopted in supercomputing […]
Nov, 3

A hybrid algorithm for parallel molecular dynamics simulations

This article describes an algorithm for hybrid parallelization and SIMD vectorization of molecular dynamics simulations with short-ranged forces. The parallelization method combines domain decomposition with a thread-based parallelization approach. The goal of the work is to enable efficient simulations of very large (tens of millions of atoms) and inhomogeneous systems on many-core processors with hundreds […]
Nov, 3

Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

In this paper we focus on the integration of high-performance numerical libraries in ab initio codes and the portability of performance and scalability. The target of our work is FLEUR, a software for electronic structure calculations developed in the Forschungszentrum J"ulich over the course of two decades. The presented work follows up on a previous […]
Nov, 2

A Survey of Techniques for Architecting TLBs

Translation lookaside buffer (TLB) caches virtual to physical address translation information and is used in systems ranging from embedded devices to high-end servers. Since TLB is accessed very frequently and a TLB miss is extremely costly, prudent management of TLB is important for improving performance and energy efficiency of processors. In this paper, we present […]
Nov, 1

Classification Performance of Convolutional Neural Networks

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. […]
Nov, 1

Towards Automating Multi-dimensional Data Decomposition for Executing a Single-GPU Code on a Multi-GPU System

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 […]
Nov, 1

Programming Heterogeneous Systems from an Image Processing DSL

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 […]
Nov, 1

LightRNN: Memory and Computation-Efficient Recurrent Neural 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 […]
Nov, 1

Performance Optimization of 3-D Lattice Boltzmann Flow Solver on a GPU

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 […]
Nov, 1

Design and Analysis of Soft-Error Resilience Mechanisms for GPU Register File

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 […]
Oct, 29

The 2nd International SYCL Workshop (SYCL 2017), 2017

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 […]
Oct, 29

Hetero-Mark, A Benchmark Suite for CPU-GPU Collaborative Computing

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 […]

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