Multicore MATLAB 

Learn about running MATLAB on multicore machines. Resources include videos, examples, and documentation related to builtin multithreading and parallelism using MATLAB workers. Submitted: Apr 18, 2012

MathWorks  Parallel Computing Toolbox 

Parallel Computing Toolbox enables you to use MATLAB and Simulink to harness multiprocessing hardware for solving computationally and dataintensive problems. The Parallel Computing Toolbox software extends the MATLAB language with highlevel parallel processing constructs such as parallel forloops, distributed arrays, parallel numerical algorithms, and messagepassing functions that let you exploit data and task parallelism in your applications. You can transition from serial MATLAB programs to parallel MATLAB programs without making significant changes to existing code or learning a lowlevel parallel language. Submitted: Nov 08, 2004

Parallel Programming with MPI 

Parallel Programming with MPI is an elementary introduction to programming parallel systems that use the MPI 1.1 library of extensions to C and Fortran. It is intended for use by students and professionals with some knowledge of programming conventional, singleprocessor systems, but who have little or no experience programming multiprocessor systems. It is an extensive revision and expansion of A User's Guide to MPI. Submitted: Jan 17, 1997

TCP/IP communication using MATLAB  Technical article 

MATLAB can be used as a client to exchange data with a remote application not developed in MATLAB. This technical article describes how Edward Mayhew from George Mason University built a distributed application using MATLAB and TCP/IP communication. Submitted: Jul 18, 2006

ParaMat  Parallel processing under MATLAB control on a multiprocessor Alpha AXP 

ParaMat is a software package that allows existing PCbased MATLAB and Simulink applications to benefit easily from a parallel processing system based on the Alpha AXP RISC processor. The underlying hardware platform is scaleable and can provide GFlops of performance to suit the application needs. No detailed knowledge of parallel processing techniques by the programmer is required. The booting of the multiprocessor network is automatic and efficient kernel level interprocessor communication facilities are provided. Application Areas: Aerospace; Application development tools; Data analysis/modeling; Educational instruction; Financial analysis/modeling/services; Hydraulics/pneumatics; Machine vision; Manufacturing; Mechanics/compression systems; Satellite design; System identification. Submitted: Apr 07, 2000

CONLAB 

CONLAB (CONcurrent LABoratory) is an environment for developing algorithms for parallel computer architectures. It is an interactive environment in which one can simulate MIMD architectures with distributed memory and communication with message passing, as well as MIMD architectures with shared memory. CONLAB is an extension of MATLAB with control structures for expressing parallel execution of programs and primitives for message passing, use of shared memory and synchronization. The language used in CONLAB is a language that is close to the informal algorithm specification languages that many algorithm designers use. Submitted: Jul 18, 1998

Mesh partitioning and graph separators Toolbox 

Solving a large problem on a parallel computer with distributed memory usually requires the data for the problem to be somehow partitioned between processors. The quality of the partition affects the speed of solution; a good partition divides the work evenly and requires as little communication as possible. Many problems can be represented as graphs. Examples are both direct and iterative methods for a sparse linear system solution [20, 40], and, more generally, many situations in which partial differential equations are solved in physical simulation and modeling. Partitioning such a problem typically comes down to dividing the vertices of the graph into sets of equal size with few edges joining vertices in different sets. This toolbox provides Partitioning methods, Multiway partitions, Vertex separators, Nested dissection, Meshes and graph generators and other utilities. Submitted: Jul 19, 1999

Parallel Implementation of the Filtered Back Projection Algorithm for Tomographic Imaging 

Computer Tomography(CT) is used in several applications  medicine, nondestructive testing/evaluation, astronomy and others to look inside objects and analyze internal structures. However, the problem in general is computationally very intensive. . . Submitted: Jun 07, 2005

Amirus Reflective Memory Software 

AmirusMM is a development tool which 'reflects' up to 64Mbytes of computer memory using standard networking hardware and the processors own exception handling mechanisms. Up to 32 computer systems can share memory areas with millisecond latency and 30Mbit/second bandwidth. The MATLAB interface allows programmers to define variables in shared memory which permit robust distributed applications to be developed quickly and simply. Submitted: Oct 09, 2003

MATLAB Distributed Computation Tutorial 

This tutorial is aimed at researchers and engineers who want to use MATLAB for distributed applications, such as the generation and processing of data on multiple machines. This kind of setup is especially useful when calculations can be executed in parallel, speeding up the process. Submitted: Jun 06, 2005

MATLAB Distributed Computing Server 4.0 

MATLAB Distributed Computing Server™ lets users solve computationally and dataintensive problems by executing MATLAB® and Simulink® based applications on a computer cluster. MATLAB Distributed Computing Server is available for all hardware platforms and operating systems supported by MATLAB and Simulink. It includes a basic scheduler and directly supports Platform LSF®, Microsoft® Windows® Compute Cluster Server, Microsoft Windows HPC Server 2008, Altair PBS Pro®, and TORQUE schedulers. Other schedulers can be integrated using the generic interface API. The product’s dynamic licensing feature frees administrators from managing the license profiles of individual users on the cluster; only a single MATLAB Distributed Computing Server license is required for the cluster. Submitted: Nov 12, 2008

並列計算 

並列計算には Parallel Computing Toolbox が最適です。マルチコア コンピューター、GPU (GPGPU)、およびコンピューター クラスター上での並列計算の実行が可能です。

