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Video: Task parallelism and data parallelism explained

Before you jump into parallel programming, you'll need to understand both task and data parallelism. In this video, Intel director James Reinders explains the difference between task and data parallelism, and how there is a way around the limits imposed by Amdahl's Law.

Before you jump into parallel programming, you'll need to understand both task and data parallelism. In this video, Intel director James Reinders explains the difference between task and data parallelism, and how there is a way around the limits imposed by Amdahl's Law.

Note: This video was originally published as part of ZDNet's Parallelism Breakthrough series - sponsored by Intel.

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Bill Detwiler is Managing Editor of TechRepublic and Tech Pro Research and the host of Cracking Open, CNET and TechRepublic's popular online show. Prior to joining TechRepublic in 2000, Bill was an IT manager, database administrator, and desktop supp...

1 comments
adornoe
adornoe

The fact remains that no matter how many processors you have and no matter how intelligent you are about splitting the work loads for data or task processing, the overall time for completion of any task or process is still dependent upon the slowest part of a computing architecture, namely, the input and output mechanism, which is most often known as hard disks. And then, you also have the data coming across communications lines which are also many, many times slower than the computer's speed. Though the computing speed and the power of the "computer" have grown tremendously over the last 30 years, the input/output speeds of the data haven't kept pace.

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