high performance computing on graphics processing units: hgpu.org

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 […]

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, […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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, […]

Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks. However, deploying deep CNN models for latency-critical tasks remains to be challenging because of the complex nature of CNNs. Recently, FPGA has become a favorable device to accelerate deep CNNs thanks to its high parallel processing capability and energy efficiency. […]

A Python non-uniform fast Fourier transform (PyNUFFT) package has been developed to accelerate multidimensional non-Cartesian image reconstruction on heterogeneous platforms. Since scientific computing with Python encompasses a mature and integrated environment, the time efficiency of the NUFFT algorithm has been a major obstacle to real-time non-Cartesian image reconstruction with Python. The current PyNUFFT software enables […]