Feb, 2

MPI-GPU parallelism in iterative eigensolvers for block-tridiagonal matrices

We consider the computation of a few eigenpairs of a generalized eigenvalue problem Ax = lambda Bx with block-tridiagonal matrices, not necessarily symmetric, in the context of Krylov methods. In this kind of computation, it is often necessary to solve a linear system of equations in each iteration of the eigensolver, for instance when B […]
Feb, 2

Analysis and implementation of a BLAST-Like algorithm for MIC architectures

Sequence alignment is becoming increasingly important in our current day and age, and with the rise of coprocessors, it is important to adapt sequence alignment algorithms to the new architecture. Parallelization using SIMD technology has previously been achieved that implement alignment algorithms e efficiently such as SWIPE, described by Rognes in 2011. The Intel Xeon […]
Feb, 2

Efficient Neural Network Acceleration on GPGPU using Content Addressable Memory

Recently, neural networks have been demonstrated to be effective models for image processing, video segmentation, speech recognition, computer vision and gaming. However, high energy computation and low performance are the primary bottlenecks of running the neural networks. In this paper, we propose an energy/performance-efficient network acceleration technique on General Purpose GPU (GPGPU) architecture which utilizes […]
Feb, 2

Optimum Application Deployment Technology for Heterogeneous IaaS Cloud

Recently, cloud systems composed of heterogeneous hardware have been increased to utilize progressed hardware power. However, to program applications for heterogeneous hardware to achieve high performance needs much technical skill and is difficult for users. Therefore, to achieve high performance easily, this paper proposes a PaaS which analyzes application logics and offloads computations to GPU […]
Feb, 2

Autotuning GPU Kernels via Static and Predictive Analysis

Optimizing the performance of GPU kernels is challenging for both human programmers and code generators. For example, CUDA programmers must set thread and block parameters for a kernel, but might not have the intuition to make a good choice. Similarly, compilers can generate working code, but may miss tuning opportunities by not targeting GPU models […]
Jan, 31

CFP: Fifth International Workshop on OpenCL (IWOCL 2017) – EXTENDED

Now in its fifth year, the International Workshop on OpenCL (IWOCL) will be hosted by The University of Toronto, Canada, at the Bahen Centre on May 16th-18th 2017. May 16th sees two activities: an Advanced Hands On OpenCL tutorial and a SYCL workshop, while May 17th and 18th will include of a mix of keynotes, […]
Jan, 26

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

As Central Processing Units (CPUs) and Graphical Processing Units (GPUs) get progressively better, different approaches and designs for implementing algorithms with high data load must be studied and compared. This work compares several different algorithm designs and parallelization APIs (such as OpenMP, OpenCL and CUDA) for both CPU and GPU platforms. We used the Cholesky […]
Jan, 26

Accelerating Workloads on FPGAs via OpenCL: A Case Study with OpenDwarfs

For decades, the streaming architecture of FPGAs has delivered accelerated performance across many application domains, such as option pricing solvers in finance, computational fluid dynamics in oil and gas, and packet processing in network routers and firewalls. However, this performance comes at the expense of programmability. FPGA developers use hardware design languages (HDLs) to implement […]
Jan, 26

Deep Convolutional Network evaluation on the Intel Xeon Phi: Where Subword Parallelism meets Many-Core

With a sharp decline in camera cost and size along with superior computing power available at increasingly low prices, computer vision applications are becoming ever present in our daily lives. Research shows that Convolutional Neural Networks (ConvNet) can outperform all other methods for computer vision tasks (such as object detection) in terms of accuracy and […]
Jan, 26

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Recent decades have witnessed the tremendous development of network science, which indeed brings a new and insightful language to model real systems of different domains. Betweenness, a widely employed centrality in network science, is a decent proxy in investigating network loads and rankings. However, the extremely high computational cost greatly prevents its applying on large […]
Jan, 26

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are significant algorithmic […]
Jan, 23

Astrophysical-oriented Computational multi-Architectural Framework

This work presents the framework for simplifying software development in the astrophysical simulations branch – Astrophysical-oriented Computational multi-Architectural Framework (ACAF). The astrophysical simulation problems are usually approximated with the particle systems for computational purposes. The number of particles in such approximations reaches several millions, which enforces the usage of the computer clusters for the simulations. […]
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