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Posts

Dec, 7

A programming framework for data streaming on the Xeon Phi

ALICE (A Large Ion Collider Experiment) is the dedicated heavy-ion detector studying the physics of strongly interacting matter and the quark-gluon plasma at the CERN LHC (Large Hadron Collider). After the second long shut-down of the LHC, the ALICE detector will be upgraded to cope with an interaction rate of 50 kHz in Pb-Pb collisions, […]
Dec, 7

MILC Code Performance on High End CPU and GPU Supercomputer Clusters

With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU processors are now commonplace, resulting in more levels of parallelism, memory hierarchy, and programming complexity. It has been necessary to adapt the MILC code to these new processors starting with NVIDIA GPUs, and more recently, the Intel Xeon Phi processors. We report on […]
Dec, 7

Study of Bandwidth Partitioning for Co-executing GPU Kernels

Co-executing GPU kernels on a partitioned GPU has been shown to improve utilization efficiency of poorly scaling tasks. While kernels can be executed in parallel, data transfers to the GPU are serial which can negatively impact parallelism and predictability of the kernels.In this work we implement a fairness-based approach to memory transfers by chunking data […]
Dec, 6

5th World Machine Learning and Deep Learning Congress, 2018

5th World Machine Learning and Deep Learning Congress welcome you to Machine Learning 2018 conference going to be held in Dubai, UAE during August 30-31, 2018 which unites brief keynote presentations, speaker talks, exhibitions, Symposiums, workshops. Machine Learning 2018 is the Congress which will be most visited by all the most innovative minds, practitioners, experts, […]
Dec, 3

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda

The world of DNA sequencing has not only been a difficult field since it was first worked upon, but it is also growing at an exponential rate. The amount of data involved in DNA searching is huge, thereby normal tools or algorithms are not suitable to handle this degree of data processing. BLAST is a […]
Dec, 3

A Hybrid-parallel Architecture for Applications in Bioinformatics

Since the advent of Next Generation Sequencing (NGS) technology, the amount of data from whole genome sequencing has been rising fast. In turn, the availability of these resources led to the tapping of whole new research fields in molecular and cellular biology, producing even more data. On the other hand, the available computational power is […]
Dec, 3

Methods for GPU Acceleration of Big Data Applications

Big Data applications are trivially parallelizable because they typically consist of simple and straightforward operations performed on a large number of independent input records. GPUs appear to be particularly well suited for this class of applications given their high degree of parallelism and high memory bandwidth. However, a number of issues severely complicate matters when […]
Dec, 3

Blocked All-Pairs Shortest Paths Algorithm on Intel Xeon Phi KNL Processor: A Case Study

Manycores are consolidating in HPC community as a way of improving performance while keeping power efficiency. Knights Landing is the recently released second generation of Intel Xeon Phi architecture. While optimizing applications on CPUs, GPUs and first Xeon Phi’s has been largely studied in the last years, the new features in Knights Landing processors require […]
Dec, 3

STAR-RT: Visual attention for real-time video game playing

In this paper we present STAR-RT – the first working prototype of Selective Tuning Attention Reference (STAR) model and Cognitive Programs (CPs). The Selective Tuning (ST) model received substantial support through psychological and neurophysiological experiments. The STAR framework expands ST and applies it to practical visual tasks. In order to do so, similarly to many […]
Nov, 30

Qualcomm Snapdragon Mobile Platform OpenCL General Programming and Optimization

This document intends to provide a detailed guidance on how to optimize OpenCL programs with Adreno GPUs. A good amount of information has been provided to help developers understand the OpenCL fundamentals and Adreno architectures, and most importantly, master OpenCL optimization techniques. OpenCL optimization is often challenging and requires a lot of trial and error. […]
Nov, 30

Implementing implicit OpenMP data sharing on GPUs

OpenMP is a shared memory programming model which supports the offloading of target regions to accelerators such as NVIDIA GPUs. The implementation in Clang/LLVM aims to deliver a generic GPU compilation toolchain that supports both the native CUDA C/C++ and the OpenMP device offloading models. There are situations where the semantics of OpenMP and those […]
Nov, 30

2D Image Convolution using Three Parallel Programming Models on the Xeon Phi

Image convolution is widely used for sharpening, blurring and edge detection. In this paper, we review two common algorithms for convolving a 2D image by a separable kernel (filter). After optimising the naive codes using loop unrolling and SIMD vectorisation, we choose the algorithm with better performance as the baseline for parallelisation. We then compare […]
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