Parallel access

Documentation Examples Functions Videos Answers Main Content Choose a parallel computing solutionParallel computing can help you to solve big computing problems in different ways. MATLAB® and Parallel Computing Toolbox™ provide an interactive programming environment to help tackle your computing tasks. If your code runs too slowly, you can profile it, vectorize it, and use built-in MATLAB parallel computing support. Then you can try to accelerate your code by using parfor on multiple MATLAB workers in a parallel pool. If you have big data, you can scale up using distributed arrays or datastore. You can also execute a task without waiting for it to complete, using parfeval, so that you can carry on with other tasks. You can use different types of hardware to solve your parallel computing problems, including desktop computers, GPUs, clusters, and clouds. To get started, see Quick Start Parallel Computing in MATLAB.Functionsexpand allKey functions in Parallel Computing ToolboxparforExecute for-loop iterations in parallel on workersparfevalRun function on parallel pool workergpuArrayArray stored on GPUdistributedCreate and access elements of distributed arrays from clientbatchRun MATLAB script or function on workerparpoolCreate parallel pool on clusterticBytesStart counting bytes transferred within parallel pooltocBytesRead how many bytes have been transferred since calling ticBytesdelete (Pool)Shut down parallel poolTopicsBasicsChoose a Parallel Computing SolutionDiscover the most important functionalities offered by MATLAB and Parallel Computing Toolbox to solve your parallel computing problem.Parallel Language Decision TablesDiscover example use cases for common parallel computing language features.Quick Start Parallel Computing in MATLABLearn about parallel computing in MATLAB and Parallel Computing Toolbox.Run MATLAB Functions with Automatic Parallel SupportTake advantage of parallel computing resources without requiring any extra coding.Interactively Run Loops in Parallel Using parforConvert a for-loop into a scalable parfor-loop.Choose How to Manage Data in Parallel ComputingDetermine the data management approach that meets your parallel computing requirements.Plot During Parameter Sweep with parforPerform a parameter sweep in parallel and plot progress during parallel computations. Scale Up from Desktop to ClusterDevelop your parallel MATLAB® code on your local machine and scale up to a cluster. Run Batch Parallel JobsUse batch to offload work from your MATLAB session to run in the background.Process Big Data in the CloudThis example shows how to access a large data set in the cloud and process it in a cloud cluster using MATLAB® capabilities for big data.Evaluate Functions in the Background Using parfevalBreak out of an optimizing loop early and collect results as they become available.Run MATLAB Functions on a

(accessing different registers) on modern pipelined processors. For example, in exemplary implementations, the Parallel Lookup Multiplier has been timed at about 15 CPU clock cycles per 64 bytes processed per CPU core (about 0.36 clock cycles per instruction). In addition, the code footprint is practically nonexistent for the Parallel Lookup Multiplier (40 instructions) compared to that of the Parallel Multiplier (about 34,300 instructions), even when factoring the 8 KB needed for the two lookup tables in the Parallel Lookup Multiplier. embodiments of the Parallel Lookup Multiplier can be passed 64 bytes of operand data (such as the next 64 bytes of surviving original data X to be processed) in four consecutive registers, whose contents can be preserved upon exiting the Parallel Lookup Multiplier (and all in the same 40 machine instructions) such that the Parallel Lookup Multiplier can be invoked again on the same 64 bytes of data without having to access main memory to reload the data. operand data such as the next 64 bytes of surviving original data X to be processed four consecutive registers whose contents can be preserved upon exiting the Parallel Lookup Multiplier (and all in the same 40 machine instructions) such that the Parallel Lookup Multiplier can be invoked again on the same 64 bytes of data without having to access main memory to reload the data. FIG. 1 For example, can generate the check data J from the original data D, and is described further with respect to FIG. 4 . the parity

Owners. Starting Parallel Inspector The analysis results are gathered together in related categories 16 Intel® Parallel Inspector Double-click a line from the Problem Sets pane to see the source code that generated the diagnostic 17 Copyright © 2006, Intel Corporation. All rights reserved. Intel and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States or other countries. *Other brands and names are the property of their respective owners. Starting Parallel Inspector The source lines involved in a data race can be shown 17 Intel® Parallel Inspector 18 Copyright © 2006, Intel Corporation. All rights reserved. Intel and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States or other countries. *Other brands and names are the property of their respective owners. 18 Intel® Parallel Inspector Activity 1a - Potential Energy Build and run serial version Build threaded version Run application in Parallel Inspector to identify threading problems 19 Copyright © 2006, Intel Corporation. All rights reserved. Intel and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States or other countries. *Other brands and names are the property of their respective owners. 19 Intel® Parallel Inspector Race Conditions Execution order is assumed but cannot be guaranteed Concurrent access of same variable by multiple threads Most common error in multithreaded programs May not be apparent at all times 20 Copyright © 2006, Intel Corporation. All rights reserved. Intel and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States or other countries. *Other brands and names are the property of their respective owners. 20 Intel® Parallel Inspector Solving Race Conditions Solution: Scope variables to be local to threads When to use Value computed is not used outside parallel region Temporary or “work” variables How to implement OpenMP scoping clauses ( private, shared ) Declare variables within threaded functions Allocate variables on thread stack TLS (Thread Local Storage) API 21 Copyright © 2006, Intel Corporation. All rights reserved. Intel and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States or other countries. *Other brands and names are the property of their respective owners. 21 Intel® Parallel Inspector Solving Race Conditions Solution: Control shared access with critical regions When to use