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Scooped by Mikael Bourges-Sevenier
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Massively parallel approximate Gaussian process regression

We explore how the big-three computing paradigms -- symmetric multi-processor (SMC), graphical processing units (GPUs), and cluster computing -- can together be brought to bare on large-data Gaussi...
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Multiphase Flow Simulations in Inclined Tubes with Lattice Boltzmann Method on GPU

Multiphase flows are widely used in many practical applications in industry, such as oil industry, chemical and thermal engineering, bioengineering and medicine. Especially flows in tubes with gran...
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Moim: A Multi-GPU MapReduce Framework

MapReduce greatly decrease the complexity of developing applications for parallel data processing. To considerably improve the performance of MapReduce applications, we design a new MapReduce frame...
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Solving Multiple Queries through a Permutation Index in GPU

Query-by-content by means of similarity search is a fundamental operation for applications that deal with multimedia data. For this kind of query it is meaningless to look for elements exactly equa...
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QCDGPU: open-source package for Monte Carlo lattice simulations on OpenCL-compatible multi-GPU systems

The multi-GPU open-source package QCDGPU for lattice Monte Carlo simulations of pure SU(N) gluodynamics in external magnetic field at finite temperature and O(N) model is developed. The code is imp...
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Work Efficient Parallel Algorithms for Large Graph Exploration

Graph algorithms play a prominent role in several fields of sciences and engineering. Notable among them are graph traversal, finding the connected components of a graph, and computing shortest pat...
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Optimization of the HEFT algorithm for a CPU-GPU environment

Scheduling applications efficiently on a network of computing systems is crucial for high performance. This problem is known to be NP-Hard and is further complicated when applied to a CPU-GPU heter...
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FlexGrip: A Soft GPGPU for FPGAs

Over the past decade, soft microprocessors and vector processors have been extensively used in FPGAs for a wide variety of applications. However, it is difficult to straightforwardly extend their f...
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WebGL around the net, 17 Oct 2013 | Learning WebGL

WebGL around the net, 17 Oct 2013 | Learning WebGL | opencl, opengl, webcl, webgl | Scoop.it
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VDBSCAN+: Performance Optimization Based on GPU Parallelism

Spatial data mining techniques enable the knowledge extraction from spatial databases. However, the high computational cost and the complexity of algorithms are some of the main problems in this ar...
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Basic Concepts: Writing OpenCL code for single and double precision - Blog - StreamComputing

Basic Concepts: Writing OpenCL code for single and double precision - Blog - StreamComputing | opencl, opengl, webcl, webgl | Scoop.it
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Empirical performance modeling of GPU kernels using active learning

We focus on a design-of-experiments methodology for developing empirical performance models of GPU kernels. Recently, we developed an iterative active learning algorithm that adaptively selects par...
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Image Sensors World: Image Processing Speedup on ARM GPU

Image Sensors World: Image Processing Speedup on ARM GPU | opencl, opengl, webcl, webgl | Scoop.it
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SIMD Parallel Gibbs Sampling of Probabilistic Directed Acyclic Graphs

We present a single-chain parallelization strategy for Gibbs sampling of probabilistic Directed Acyclic Graphs, where contributions from child nodes to the conditional posterior distribution of a g...
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2HOT: An Improved Parallel Hashed Oct-Tree N-Body Algorithm for Cosmological Simulation

We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, wit...
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Concurrent kernel execution on Graphic Processing Units

General Purpose Graphic Processing Unit (GPGPU) are now used in high performance computing (HPC) for their massively parallel computing aspect and capabilities. Those devices integrate hundreds of ...
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An OpenCL-based Implementation of H.264 Encoder

We present an accelerated implementation of high-speed video stream encoder for the H.264 digital video codec standard. Based on the parallel processing techniques with GPU's, we used an OpenCL-bas...
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Architecture-and Workload-Aware Heterogeneous Algorithms for Sparse Matrix Vector Multiplication

Multiplying a sparse matrix with a vector, denoted spmv, is a fundamental operation in linear algebra with several applications. Hence, efficient and scalable implementation of spmv has been a topi...
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Energy Efficiency Studies of Mont Blanc Applications

In this thesis, the performance and energy efficiency of four different implementations of matrix multiplication, written in OmpSs and OpenCL, is tested and evaluated. The benchmarking is done usin...
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Efficient fine grained shared buffer management for multiple OpenCL devices

OpenCL programming provides full code portability between different hardware platforms, and can serve as a good programming candidate for heterogeneous systems, which typically consist of a host pr...
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Multi-Scale Scheduling Techniques for Signal Processing Systems

A variety of hardware platforms for signal processing has emerged, from distributed systems such as Wireless Sensor Networks (WSNs) to parallel systems such as Multicore Programmable Digital Signal...
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Dynamic Load Balancing in GPU-Based Systems - Early Experiments

The dynamic load-balancing framework in Charm++/AMPI, developed at the University of Illinois, is based on using processor virtualization to allow thread migration across processors. This framework...
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Early Experiences in Running Many-Task Computing Workloads on GPGPUs

This work aims to enable Swift to efficiently use accelerators (such as NVIDIA GPUs) to further accelerate a wide range of applications. This work presents preliminary results in the costs associat...
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Easy OpenCL with Python from Dr.Dobbs | LEAP Blog and Conference

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Understanding and Modeling the Synchronization Cost in the GPU Architecture

Graphic Processing Units (GPUs) have been growing more and more popular being used for general purpose computations. GPUs are massively parallel processors which make them a much more ideal fit for...
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