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The Paralation Model: Architecture-Independent Parallel Programming (Artificial Intelligence)

Gary W. Sabot · 3 HN comments
HN Books has aggregated all Hacker News stories and comments that mention "The Paralation Model: Architecture-Independent Parallel Programming (Artificial Intelligence)" by Gary W. Sabot.
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Amazon Summary
The Paralation Model introduces a way of programming parallel computers that is easy to use for general problem solving, and will work for many different parallel computer architectures with any number of processors, from one to billions. The book includes working LISP source code for a mini compiler, along with many programming examples. Parallel computers can often be impossibly hard to program. The paralation model that Gary Sabot describes is a breakthrough in its simplicity and well defined semantics and can serve as a useful and stable semantic staging point for parallel language research. Consisting of a new data structure and a small, irreducible set of operators, the model has a number of useful features: it can be combined with any base language to produce a concrete parallel language; it makes explicit and transparent the cost of both processing and communication, often ignored by shared memory and data flow models; and it serves as a precise tool for a programmer while simultaneously supplying a compiler with an abundance of useful information for a variety of target architectures. This decouples advances and changes in parallel computer design from the design of application programs: old paralation programs can take advantage of new computers without sacrificing efficiency. Gary Sabot is a research scientist in the advanced architecture group of Thinking Machines Corporation where he is helping to design future generations of parallel supercomputers. The Paralation Model is included in the Artificial Intelligence series, edited by Patrick Winston, Michael Brady, and Daniel Bobrow.
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That sounds to me like Gary Sabot's ‘Paralation Model’.

https://www.amazon.com/Paralation-Model-Architecture-Indepen...

> I think easily and uniformly programming disparate compute devices (CPUs, SIMD, GPUs, FPGAs, ISPs, DSPs, and eventually quantum) is the next BIG problem in programming languages.

The biggest challenge to producing a unified programming model across all of these is dealing with moving data around. CPU vs GPU vs FPGA all have very different memory access characteristics. Gary Sabot was the first to try to tackle the problem of dealing with memory layout/locality in a language with what he called the Paralation model (http://www.amazon.com/The-Paralation-Model-Architecture-Inde...) and a lot of that work carried over into NESL (http://www.cs.cmu.edu/~scandal/nesl.html). Hadoop is a very lobotomized and hard to use modern version of these ideas.

I wouldn't knock DSLs - a lot of people are having good results using DSLs to program FPGAs (for example that's pretty much what http://www.novasparks.com/ does - http://lisp-univ-etc.blogspot.com/2013/06/lisp-hackers-marc-...).

⬐ dmmalam
Yea, totally agree that memory is at the heart of the problem. Not only in needing a unified view, but also in bandwidth constraints that seem to be the bottleneck in so much code (von neumann bottleneck).

I think a real solution would not just be software only, it would include a hardware component (or tuned to work with a particular chip): a SoC with a few fat cpu cores, many lite core, a GPU, some fpga fabric, all sharing a large cache subsystem and memory.

I think you might like this book: The Paralation Model, by Gary W. Sabot.

http://www.amazon.com/Paralation-Model-Architecture-Independ...

⬐ denimboy
Interesting. Found this article which explains the paralation model.

http://www.mactech.com/articles/mactech/Vol.08/08.07/Paralat...

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