The July 2020 edition of the Communications of the ACM (CACM) had a front-page theme of “Domains-Specific Hardware Accelerators”, or DSAs. It contained two articles about the subject, one about an academic genomics accelerator, and one about the Google TPU. Hardware accelerators dedicated to particular types of computation are basically everywhere today, and an accepted part of the evolution of computers. The CACM articles have some good tidbits and points about how accelerators are designed and used today. At the same time, I also found a youtube talk about the first hardware accelerator, the IBM Stretch HARVEST, showing both contrasts with today as well as a remarkable continuity in concept.

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When does Hardware Acceleration make Sense in Networking?

q_stampYes, when does hardware acceleration make sense in networking? Hardware acceleration in the common sense of “TCP offload”. This question was answered by a very nicely reasoned “no” in an article by Mike Odell in ACM Queue called “Network Front-End Processors, Yet Again“.

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IBM z6: Multicore, Accelerators

z6 die photoThe IBM mainframe family started with the S/360 back in the 1960s is still going strong. The naming has been a interesting in recent years, going from S/390 to z900 to z990 to z9.

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Homogeneous and Heterogeneous Multicore vs Programmers

An old colleague just sent me an email bringing up a discussion we had last year, where he was a strong proponent for the homogeneous model of a multiprocessor. The root of that discussion was the difference between the Xbox 360 and Playstation 3 processors. The Xbox 360 has a three-core, two-threads-per-core homogeneous PowerPC main processor called the Xenon (plus a graphics processor, obviously), while the PS3 has a Cell processor with a single two-threaded PowerPC core and seven SPEs, Synergistic Processing Elements (basically DSP-like SIMD machines).

In the game business, it is clear that the Xenon CPU is considered easier to code for. This means that even though the Cell processor clearly has higher theoretical raw performance, in practical the two machines are about equal in power since it is harder to make use of the Cell. Which seems to be a fact.

So here, homogeneous systems do appear to have it easier among programmers. However, I do not believe that that extends to all systems, all the time, everywhere.

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