Automatic parallelization

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Automatic parallelization, also auto parallelization, or autoparallelization refers to converting sequential <a href="/facts/Source_code/o9J6Jv23">code</a> into <a href="/facts/Multi-threaded/hh8WHyPd">multi-threaded</a> and/or <a href="/facts/Automatic_vectorization/YzD5uHsX">vectorized</a> code in order to use multiple processors simultaneously in a shared-memory <a href="/facts/Multiprocessor/aDCfehDQ">multiprocessor</a> (<a href="/facts/Symmetric_multiprocessing/T9CahUsN">SMP</a>) machine. Fully automatic parallelization of sequential programs is a challenge because it requires complex <a href="/facts/Program_analysis_(computer_science)/oaJTAjhH">program analysis</a> and the best approach may depend upon parameter values that are not known at compilation time.
</p><p>The programming control structures on which autoparallelization places the most focus are <a href="/facts/Control_flow/2XTDfErC">loops</a>, because, in general, most of the <a href="/facts/Run_time_(program_lifecycle_phase)/lCxKsrzB">execution time</a> of a program takes place inside some form of loop.
There are two main approaches to parallelization of loops: pipelined multi-threading and cyclic multi-threading. For example, consider a loop that on each iteration applies a hundred operations, and runs for a thousand iterations.  This can be thought of as a grid of 100 columns by 1000 rows, a total of 100,000 operations. Cyclic multi-threading assigns each row to a different thread. Pipelined multi-threading assigns each column to a different thread.
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Automatic parallelization open-in-new

Automatic parallelization