HPC PROJECTS: MULTICORE
Multiple cores multiply programming
The transition from applications written for sequential execution to those that can take advantage of multicore architectures has taken on enormous importance and brought with it some challenging problems. Paul Schreier examines some of the tools that are available to help programmers parallelise their code
H
PC has the potential to benefit enormously from the trend of placing multiple cores on each
processor chip. Suddenly, in virtually every desktop machine, multiple cores are available to speed up large tasks that can be broken into multiple parallel threads. Software, however, hasn’t been keeping
pace with this hardware trend. For instance, market research company IDC has heard from some users, for the first time, that legacy code sometimes runs more slowly after they upgrade to the latest multicore hardware. Most of today’s code isn’t optimised for multicore operation, and in some cases the overhead of passing data among cores degrades performance. Meanwhile, the entire industry has recognised the importance of multicore code, and a number of companies have jumped in with tools that help developers
create code that exploits the potential of today’s hardware and that can scale up along with HPC systems.
Speed-up depends on the application
How much of a performance increase is possible? On this topic, many people refer to Ahmdahl’s Law, which states that the speed-up of a program using multiple parallel processors is limited by the fraction of sequential code in a program. For example, if 95 per cent of a program can be parallelised, the theoretical maximum speedup is 20x – no matter how many processors are used. This exercise points out the necessity to focus programming improvements on those parts of a code that can actually benefit from parallelisation and also that not every code will benefit from a multicore architecture.
Start at the chip level
How can we exploit parallelism in smaller configurations; in the HPC world, how can we take advantage of parallelism at the node level? These are some of the issues we face today, says James Reinders, a multicore evangelist at Intel. Five years ago, scientists were more concerned with distributed computing, but today there’s a surge in interest on how to improve performance on a single node. For this purpose, Intel offers a number of multicore programming tools and will soon be introducing even more. Start with Intel’s Parallel Studio, which is an add-on for Microsoft’s Visual Studio that adds C/C++ parallelism. Tools in the Studio augment the included C++ compiler: with Parallel Composer you can introduce threads, compile and debug code; with Parallel Inspector you find threading and memory errors; Parallel Amplifier helps in tuning ➤
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SCIENTIFIC COMPUTING WORLD JUNE/JULY 2010
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