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Posts

Apr, 15

DLL: A Blazing Fast Deep Neural Network Library

Deep Learning Library (DLL) is a new library for machine learning with deep neural networks that focuses on speed. It supports feed-forward neural networks such as fully-connected Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs). It also has very comprehensive support for Restricted Boltzmann Machines (RBMs) and Convolutional RBMs. Our main motivation for this […]
Apr, 15

Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs and Its Evaluation

Recently, C-based OpenCL design environment is proposed to design FPGA (field programmable gate array) accelerators. Although many C-programs can be executed on FPGAs, the best c-code for a CPU may not be the most appropriate one for an FPGA. Users must have some knowledge about computer architecture in order to write a good OpenCL code. […]
Apr, 15

Implementing Push-Pull Efficiently in GraphBLAS

We factor Beamer’s push-pull, also known as direction-optimized breadth-first-search (DOBFS) into 3 separable optimizations, and analyze them for generalizability, asymptotic speedup, and contribution to overall speedup. We demonstrate that masking is critical for high performance and can be generalized to all graph algorithms where the sparsity pattern of the output is known a priori. We […]
Apr, 15

G-NET: Effective GPU Sharing in NFV Systems

Network Function Virtualization (NFV) virtualizes software network functions to offer flexibility in their design, management and deployment. Although GPUs have demonstrated their power in significantly accelerating network functions, they have not been effectively integrated into NFV systems for the following reasons. First, GPUs are severely underutilized in NFV systems with existing GPU virtualization approaches. Second, […]
Apr, 15

Towards a Unified CPU-GPU code hybridization: A GPU Based Optimization Strategy Efficient on Other Modern Architectures

In this paper, we suggest a different methodology to shorten the code optimization development time while getting a unified code with good performance on different targeted devices. In the scope of this study, experiments are illustrated on a Discontinuous Galerkin code applied to Computational Fluid Dynamics. Tests are performed on CPUs, KNL Xeon-Phi and GPUs […]
Apr, 7

Evaluating Performance Tradeoffs on the Radeon Open Compute Platform

GPUs have been shown to deliver impressive computing performance, while also providing high energy efficiency, across a wide range of high-performance and embedded system workloads. However, limited support for efficient communication and synchronization between the CPU and the GPU impacts our ability to fully exploit the benefits of heterogeneous systems. Recently, the Heterogeneous System Architecture […]
Apr, 7

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest "link" in the security "chain". In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key […]
Apr, 7

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases […]
Apr, 7

Verification of Program Parallelization

This thesis presents techniques to improve reliability and prove functional correctness of parallel programs. These requirements are especially crucial in critical systems where system failures endanger human lives, cause substantial economic damages or security breaches. Today’s critical systems are expected to deliver more and more complex and computationally intensive functions. In many cases these cannot […]
Apr, 7

Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments

Architectures with multiple classes of memory media are becoming a common part of mainstream supercomputer deployments. So called multi-level memories offer differing characteristics for each memory component including variation in bandwidth, latency and capacity. This paper investigates the performance of sparse matrix multiplication kernels on two leading high-performance computing architectures — Intel’s Knights Landing processor […]
Mar, 31

HDArray: Parallel Array Interface for Distributed Heterogeneous Devices

Heterogeneous clusters with nodes containing one or more accelerators, such as GPUs, have become common. While MPI provides a mechanism and management of inter-address space communication, and OpenCL provides a way to manage computation and communication within a process with access to heterogeneous computational resources, programmers are forced to write hybrid programs that manage the […]
Mar, 31

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Volume rendering is an important area of study in computer graphics, due to its application in areas such as medicine, physic simulations, oil and gas industries, and others. The main used method nowadays for volume rendering is ray casting. Nevertheless, there are a variety of parallel APIs that can be used to implement it. Thus, […]
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