parallelise
parallelize

I have large, floating-point intensive jobs that don't parallelise well.

It also allows individual tiles to be pre-computed, a task easy to parallelise.

The solution was to massively parallelise their system architecture.

Learn how to parallelise algorithms for big dataset processing.

Computer chess algorithms are notoriously difficult to parallelise efficiently.

Chess is a highly irregular problem which is notoriously difficult to parallelise efficiently.

In particular, the approach could be combined with approaches to parallelise the non-bonded interactions, permitting parallelisation over several thousand processors.

The programmer wraps code they wish to parallelise inside a lexical scope, which is tagged as 'sieve'.

Parallelise this

Because it can handle multiple simultaneous interactions with clients, it provides an easy way to parallelise (and so, to greatly accelerate) many compute-intensive modelling applications, including parameter studies.

Forge 90 will also be available to run High Performance Fortran parallel programs on the SP1 and to help parallelise existing serial Fortran programs.

The PSE parallelise and embed many individual numerical calculations in an individual numerical calculations in an industrial serial optimisation code.

The following topics will be covered: All the important directives How to parallelise your code (plenty of hands-on exercises with emphasis to the practical use of OpenMP).

It boasts a suite of accurate performance monitoring tools, supporting high quality visualisation of parallel profiles, and has been used to help parallelise several large programs, including the 47,000 line Lolita natural language processor.

Increasing support for functional programming in mainstream languages used commercially, including pure functional programming for making code easier to reason about and easier to parallelise (at both micro- and macro- levels)

The toolkit, developed in conjunction with Steria SA, Parsytec GmbH and other European research institutes, is designed to parallelise Fortran-based applications for multiple instruction multiple data (MIMD) architectures.

Operation can sometimes be unpredictable since APL defines that computers with vector-processing capabilities should parallelise and may reorder array operations as far as possible - therefore, test and debug user functions particularly if they will be used with vector or even matrix arguments.

With careful design of hardware and microcode, this property can be exploited to parallelise operations that use different areas of the CPU; for example, in the case above, the ALU is not required during the first tick, so it could potentially be used to complete an earlier arithmetic instruction.

Eventually, a similar idea may be used to parallelize the problem.

Some tools parallelize only special form of code like loops.

Analyze the loops to find those that are safe to parallelize.

It is also possible to parallelize over both inner and outer loops.

It is also harder to implement and parallelize compared to a merge sort.

Those loops are not only hard to parallelize, but they also perform horribly.

If single-processor programs, do you want to parallelize them?

However, this option tells the compiler it safe to parallelize the loop.

Example: Do not unroll or parallelize loops if it increases code size.

In essence there are 4 steps that should be considered to "parallelize" your code:

It is thus interesting to parallelize them.

Additionally, it is difficult to parallelize the partitioning step efficiently in-place.

If this is the case, there is ways to tell the compiler to parallelize anyhow.

Embarrassingly parallel applications are considered the easiest to parallelize.

Parallelize when loop contains a scalar used outside of loop.

In that case, the first line tells the compiler to parallelize the DO loop directly following it.

If you are sure this is not the case, you can direct the compiler to pipeline/parallelize anyway.

All of the time consuming parts of the system are parallelize and roughly linear time.

Dependence analysis determines whether or not it is safe to reorder or parallelize statements.

"We can massively parallelize the process," says Burstein.

As with the case of a loop that contains multiple exits, this loop is not safe to parallelize.

Ideas to parallelize the solution of initial value problems go back even further: the first paper proposing a parallel-in-time integration method appeared in 1964.

For this reason, it is not safe to parallelize the loop, since the compiler has no way of predicting the loop's runtime behavior.

Often this task is difficult since the programmer who wants to parallelize the code has not originally written the code under consideration.

No special attempt was made to vectorize or parallelize the code; only ordinary optimizing compilers were used.