From the recent announcements of the chip majors , it seems the catch up games that they have been playing with each other are over.
What am I talking about ? First it was all about pushing clock speeds up , and more recently it’s been about putting more cores on a chip more efficiently than the other . But as the multicore architectures become more mature , chip majors like Intel and AMD have started to diverge on their multicore strategies . Intel is moving towards homogeneous cores while AMD and IBM look to be adopting a more heterogeneous approach .

Homogeneous as the word implies means all the processing units (cores) on a certain processor are of the same type and divide the workload between them for maximum efficiency . The heterogeneous approach is a little more complicated as it may be implemented in a lot of ways , different size cores for different dedicated functions is one approach . Another approach is where a problem’s workload is split between a general-purpose processor and one or more specialized, problem-specific processors. Heterogeneous computing is a broader research area and the concept has been around for a while now, it also encompasses efforts like GPGPU computing , the Clearspeed accelerators and more recent efforts like the AGEIA PHYSX co processor .The most recent and the best example of a heterogeneous processor would be the STI Cell B.E with its Power PC core and the 8 synergistic processor units . AMD has plans for a similar architecture and their first heterogeneous multicore offering may be a CPU and GPU in the client space.

On the software side ,task-level parallelism and workload partitioning continue to be the dominant issues for multi-core platforms for both heterogeneous and homogeneous architectures. These issues will be more acute on heterogeneous multi-core systems, since the specialized processors will throw up a new set of problems. I believe heterogeneous computing is geared towards extreme performance computing , the GPGPU movement and the Cell’s performance with folding@home proves this point. General purpose computing might go the homogeneous way , as the challenges are far lesser and are fast being resolved .

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