HPCwire: HPC@Intel: When to Say No to Parallelism

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January 14, 2009

HPC@Intel: When to Say No to Parallelism

by Sanjiv Shah, Principle Engineer, Intel Software and Solutions Group

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As a software developer, you are faced with a range of options as you decide whether and how to modify your applications for parallel architectures. What approach should you adopt? How significantly should you alter applications? When should you say “no” to parallelism? Answering these questions requires not only technical expertise but also strategic thinking that evaluates the business benefits and costs. As you weigh your options, consider the suggestions below, which we’ve derived from our experiences at Intel working with software developers to optimize code.

Don't Parallelize Un-Optimized Serial Code

There is no doubt that parallelization is an important means of upgrading application performance for current and future generations of hardware. Still, threading should not be the first course of action in optimizing an application. Identify whether you can meet your performance targets using serial and vector optimizations. In some cases, you might gain greater performance improvements by optimizing the serial version than by creating a parallel one.

Don't Parallelize If the Serial Code Is Running Fast Enough Already

Although you can and should position your code for future architectures, it might not be time- and cost-effective to work to meet requirements that have not yet been created. Developing code with a higher degree of parallelism than current architectures can support is rarely the best use of resources. And of course, if your code is I/O- or memory-bound, parallelizing the code will not help.

Don't spend time trying to parallelize all your past work immediately. Think parallel when building new code or rebuilding sections of existing code. Rather than solving problems in sequential steps, consider how those problems can be broken into separate pieces that can be performed simultaneously.

As you consider how much parallelism is needed for the longer term, try to determine how quickly the application workload will scale and how that scaling will affect computational requirements. The amount of computation might rise linearly with the size of the data set, or it might rise at a geometrically faster or slower rate. (If the workload scales linearly with the data, beware that the code will run into an I/O bottleneck at some point.)

Take for example an audio processing application that can already handle a sufficient number of channels at a good sampling rate. This application might be a poor candidate for parallelization. On the other hand, a physical modeling application might be a good candidate. With higher computational power available, the modeling application could deliver finer modeling and more accurate algorithms.

Overall, make sure the gains from parallelizing an application are not offset by delays in shipping the next version of your product.

Don't Parallelize by Rewriting Code from Scratch

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