Parallel Sparse Approximate Inverse Preconditioning on Graphic Processing Units

October 22nd, 2012

Abstract:

Accelerating numerical algorithms for solving sparse linear systems on parallel architectures has attracted the attention of many researchers due to their applicability to many engineering and scientific problems. The solution of sparse systems often dominates the overall execution time of such problems and is mainly solved by iterative methods. Preconditioners are used to accelerate the convergence rate of these solvers and reduce the total execution time. Sparse Approximate Inverse (SAI) preconditioners are a popular class of preconditioners designed to improve the condition number of large sparse matrices and accelerate the convergence rate of iterative solvers for sparse linear systems. We propose a GPU accelerated SAI preconditioning technique called GSAI, which parallelizes the computation of this preconditioner on NVIDIA graphic cards. The preconditioner is then used to enhance the convergence rate of the BiConjugate Gradient Stabilized (BiCGStab) iterative solver on the GPU. The SAI preconditioner is generated on average 28 and 23 times faster on the NVIDIA GTX480 and TESLA M2070 graphic cards respectively compared to ParaSails (a popular implementation of SAI preconditioners on CPU) single processor/core results. The proposed GSAI technique computes the SAI preconditioner in approximately the same time as ParaSails generates the same preconditioner on 16 AMD Opteron 252 processors.

(Maryam Mehri Dehnavi, David Fernandez, Jean-Luc Gaudiot and Dennis Giannacopoulos: “Parallel Sparse Approximate Inverse Preconditioning on Graphic Processing Units”, IEEE Transactions on Parallel and Distributed Systems (to appear). [DOI])

CUDA 5 Production Release Now Available

October 15th, 2012

The CUDA 5 Production Release is now available as a free download at www.nvidia.com/getcuda.
This powerful new version of the pervasive CUDA parallel computing platform and programming model can be used to accelerate more of applications using the following four (and many more) new features.

• CUDA Dynamic Parallelism brings GPU acceleration to new algorithms by enabling GPU threads to directly launch CUDA kernels and call GPU libraries.
• A new device code linker enables developers to link external GPU code and build libraries of GPU functions.
• NVIDIA Nsight Eclipse Edition enables you to develop, debug and optimize CUDA code all in one IDE for Linux and Mac OS.
• GPUDirect Support for RDMA provides direct communication between GPUs in different cluster nodes

As a demonstration of the power of Dynamic Parallelism and device code linking, CUDA 5 includes a device-callable version of the CUBLAS linear algebra library, so threads already running on the GPU can invoke CUBLAS functions on the GPU. Read the rest of this entry »

Webinar: Portability, Scalability, and Numerical Stability in Accelerated Kernels

October 11th, 2012

Seeing speedups of an accelerated application is great, but what does it take to build a codebase that will last for years and across architectures? In this webinar, John Stratton will cover some of the insights gained at the University of Illinois at Urbana-Champaign from experience with computer architecture, programming languages, and application development.

The webinar will offer three main conclusions including:

  1. Performance portability should be more achievable than many people think.
  2. The number one performance-limiting factor now and in the future will be parallel scalability.
  3. As much as we care about performance, general libraries that will last have to be reliable as well as fast.

Register at http://www.gputechconf.com/page/gtc-express-webinar.html

MicroCFD now runs on CUDA enabled NVIDIA GPUs

October 11th, 2012

The MicroCFD Virtual Wind Tunnel, Educational & Professional Edition, has recently been upgraded. The new version (1.8) supports multi-core CPUs and CUDA enabled GPUs and runs
significantly faster than the previous single-processor version. The results of a benchmark test on a system with an Intel quad-core CPU and an NVIDIA 96-core GPU show that an unsteady 2D or axis-symmetric compressible flow can now be run at a resolution of one million cells (Pro Edition) within a few minutes. A 3D version is currently under development and is expected to be released in 2014.

AMD CodeXL: comprehensive developer tool suite for heterogeneous compute

October 9th, 2012

AMD CodeXL is a new unified developer tool suite that enables developers to harness the benefits of CPUs, GPUs and APUs. It includes powerful GPU debugging, comprehensive GPU and CPU profiling, and static OpenCL™ kernel analysis capabilities, enhancing accessibility for software developers to enter the era of heterogeneous computing. AMD CodeXL is available for free, both as a Visual Studio® extension and a standalone user interface application for Windows® and Linux®.

AMD CodeXL increases developer productivity by helping them identify programming errors and performance issues in their application quickly and easily. Now developers can debug, profile and analyze their applications with a full system-wide view on AMD APU, GPU and CPUs.

AMD CodeXL user group (requires registration) allows users to interact with the CodeXL team, provide feedback, get support and participate in the beta surveys.

CfP: Workshop on Parallel Computing and Optimization PCO’13

September 25th, 2012

PCO13 is to be held in conjunction with IEEE IPDPS, Boston, USA, May 20-24, 2013. Paper Submission Deadline: December 21, 2012.

The workshop on Parallel Computing and Optimization aims at providing a forum for scientific researchers and engineers on recent advances in the field of parallel or distributed computing for difficult combinatorial optimization problems, like 0-1 multidimensional knapsack problems and cutting stock problems, large scale linear programming problems, nonlinear optimization problems and global optimization problems. Emphasis will be placed on new techniques for the solution of these difficult problems like cooperative methods for integer programming problems and polynomial optimization methods. Aspects related to Combinatorial Scientific Computing (CSC) will also be treated. Finally, the use of new approaches in parallel computing like GPU or hybrid computing, peer to peer computing and cloud computing will be considered. Application to planning, logistics, manufacturing, inance, telecommunications and computational biology will be considered.

Read the rest of this entry »

Accelerating CFD using OpenFOAM with GPUs

September 23rd, 2012

The OpenFOAM CFD Toolbox is a free, open source CFD software package produced by OpenCFD Ltd. Its user base represents a wide range of engineering and science disciplines in both commercial and academic organizations. OpenFOAM has an extensive range of features to solve a wide range of fluid flows and physics phenomenon. OpenFOAM provides tools for all three stages of CFD, preprocessing, solvers, and post processing. Almost all are capable of being run in parallel as standard making it an important resource for a wide range of scientists and engineers using HPC for CFD.

General-purpose Graphics Processing Unit (GPU) technology is increasingly being used to accelerate compute-intensive HPC applications across various disciplines in the HPC community. OpenFOAM CFD simulations can take a significant amount of time and are computationally intensive. Comparing various alternatives for enabling faster research and discovery using CFD is of key importance. SpeedIT libraries from Vratis provide GPU-accelerated iterative solvers that replace the iterative solvers in OpenFOAM.

In order to investigate the GPU-acceleration of OpenFOAM, we simulate the three dimensional lid-driven cavity problem based on the tutorial provided with OpenFOAM. The 3D lid-driven cavity problem is an incompressible flow problem solved using OpenFOAM icoFoam solver. The majority of the computationally intensive portion of the solver is the pressure equation. In the case of acceleration, only the pressure calculation is offloaded to the GPUs. On the CPUs, the PCG solver with DIC preconditioner is used. In the GPU-accelerated case, the SpeedIT 2.1 algebraic multigrid precoditioner with smoothed aggregation (AMG) in combination with the SpeedIT Plugin to OpenFOAM is used.

Webinar: Scaling Soft Matter Physics to a Thousand GPUs and Beyond

September 22nd, 2012

The “Ludwig” lattice Boltzmann fluid dynamics application is a versatile application capable of simulating the hydrodynamics of complex fluids, (e.g. mixtures, surficants, liquid crystals, particle suspensions) to allow cutting-edge research into condensed matter physics. On October 3, Dr. Alan Gray from the University of Edinburgh presents a webinar on his team’s experiences in scaling the application on the Cray XK6 hybrid supercomputer. The presentation will cover:

  • A review of excellent scaling up to O(1000) GPUs
  • Steps taken to maximize performance on each GPU
  • Designing the communication to allow efficient usage of many GPUs in parallel, including the overlapping of several stages using CUDA stream functionality
  • Advanced functionality, including how to include colloidal particles in the simulation while minimizing data transfer overheads

Register at http://www.gputechconf.com/page/gtc-express-webinar.html.

NSF Sponsors Nationwide OpenACC Workshop

September 22nd, 2012

Recognizing the growing interest and demand from NSF researchers for education on GPU computing, leading centers in NSF’s Extreme Science and Engineering Discovery Environment (XSEDE) program are working together to host a free two-day, hands-on workshop to share tips and best practices for accelerating scientific applications on GPUs using OpenACC. More information: http://blogs.nvidia.com/2012/09/u-s-scientists-nsf-to-host-nationwide-gpu-computing-workshop/

Symposium on Personal High-Performance Computing

September 20th, 2012

The Vrije Universiteit Brussel, Erasmus Hogeschool Brussel and Lessius Hogeschool have the pleasure to invite you to a symposium on Personal High-Performance Computing. The symposium aims at bringing together academia and industry to discuss recent advances in using accelerators such as GPUs or FPGAs for speeding up computational-intensive applications. We target single systems such as PCs, laptops or processor boards, hence the name ‘personal’ HPC.

Scientists are encouraged to submit abstracts to be presented at the poster session. All information can be found at https://sites.google.com/site/phpc2012bxl.

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