Feb, 26

Large-Scale Stochastic Learning using GPUs

In this work we propose an accelerated stochastic learning system for very large-scale applications. Acceleration is achieved by mapping the training algorithm onto massively parallel processors: we demonstrate a parallel, asynchronous GPU implementation of the widely used stochastic coordinate descent/ascent algorithm that can provide up to 35x speed-up over a sequential CPU implementation. In order […]
Feb, 26

First Experiences Optimizing Smith-Waterman on Intel’s Knights Landing Processor

The well-known Smith-Waterman (SW) algorithm is the most commonly used method for local sequence alignments. However, SW is very computationally demanding for large protein databases. There exist several implementations that take advantage of computing parallelization on many-cores, FPGAs or GPUs, in order to increase the alignment throughtput. In this paper, we have explored SW acceleration […]
Feb, 22

Dynamic Buffer Overflow Detection for GPGPUs

Buffer overflows are a common source of program crashes, data corruption, and security problems. In this work, we demonstrate that GPU-based workloads can also cause buffer overflows, a problem that was traditionally ignored because CPUs and GPUs had separate memory spaces. Modern GPUs share virtual, and sometimes physical, memory with CPUs, meaning that GPU-based buffer […]
Feb, 22

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

MCBooster is a header-only, C++11-compliant library that provides routines to generate and perform calculations on large samples of phase space Monte Carlo events. To achieve superior performance, MCBooster is capable to perform most of its calculations in parallel using CUDA- and OpenMP-enabled devices. MCBooster is built on top of the Thrust library and runs on […]
Feb, 22

A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks

FPGA-based hardware accelerators for convolutional neural networks (CNNs) have obtained great attentions due to their higher energy efficiency than GPUs. However, it is challenging for FPGA-based solutions to achieve a higher throughput than GPU counterparts. In this paper, we demonstrate that FPGA acceleration can be a superior solution in terms of both throughput and energy […]
Feb, 22

Efficient Large-scale Approximate Nearest Neighbor Search on the GPU

We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two-level product and vector quantization tree that reduces the number of vector comparisons required during tree traversal. Our approach also includes a novel highly parallelizable re-ranking method for candidate vectors […]
Feb, 22

Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach

Cyber-epidemics, the widespread of fake news or propaganda through social media, can cause devastating economic and political consequences. A common countermeasure against cyber-epidemics is to disable a small subset of suspected social connections or accounts to effectively contain the epidemics. An example is the recent shutdown of 125,000 ISIS-related Twitter accounts. Despite many proposed methods […]
Feb, 19

A Survey of Soft-Error Mitigation Techniques for Non-Volatile Memories

Non-volatile memories (NVMs) offer superior density and energy characteristics compared to the conventional memories; however, NVMs suffer from severe reliability issues that can easily eclipse their energy efficiency advantages. In this paper, we survey architectural techniques for improving the soft-error reliability of NVMs, specifically PCM (phase change memory) and STT-RAM (spin transfer torque RAM). We […]
Feb, 18

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

Heterogeneous systems are widespread. When neatly used, they enable an impressive performance increase. However, they typically demand developers to combine multiple programming models, languages and tools into very complex programs that are hard to design and debug. Writing correct heterogeneous programs is difficult, achieving good performance is even harder. To help developers, many high-level solutions […]
Feb, 18

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

We propose a parallel graph-based data clustering algorithm using CUDA GPU, based on exact clustering of the minimum spanning tree in terms of a minimum isoperimetric criteria. We also provide a comparative performance analysis of our algorithm with other related ones which demonstrates the general superiority of this parallel algorithm over other competing algorithms in […]
Feb, 18

LAMMPS’ PPPM Long-Range Solver for the Second Generation Xeon Phi

Molecular Dynamics is an important tool for computational biologists, chemists, and materials scientists, consuming a sizable amount of supercomputing resources. Many of the investigated systems contain charged particles, which can only be simulated accurately using a long-range solver, such as PPPM. We extend the popular LAMMPS molecular dynamics code with an implementation of PPPM particularly […]
Feb, 18

Trie Compression for GPU Accelerated Multi-Pattern Matching

Graphics Processing Units allow for running massively parallel applications offloading the CPU from computationally intensive resources, however GPUs have a limited amount of memory. In this paper a trie compression algorithm for massively parallel pattern matching is presented demonstrating 85% less space requirements than the original highly efficient parallel failure-less aho-corasick, whilst demonstrating over 22 […]
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