Sieve C++ Parallel Programming System: Difference between revisions

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The '''Sieve C++ Parallel Programming System''' is a [[C++]] [[compiler]] and parallel runtime designed and released by [[Codeplay]] that aims to simplify the [[Parallel Computing|parallelization]] of code so that it may run efficiently on multi-processor or multi-core systems. It is an alternative to other well known parallelisation methods such as [[OpenMP]], the [[Rapidmind Development Platform]] and [[Intel Threading Building Blocks]].

==Introduction==

Sieve is a C++ compiler that will take a section of code (marked with [[sieve]] markers) and parallelize it automatically.

Sieve is a C++ compiler that will take a section of serial code, which is annotated with sieve markers, and parallelize it automatically. The programmer wraps code they wish to parallelise inside a [[lexical scope]], which is tagged as 'sieve'. Inside this scope, referred to commonly as a 'sieve block', certain rules apply[http://www.codeplay.com/downloads_public/sievepaper-2columns-normal.pdf]:

* All [[Side effect (computer science) | side-effects]] within the sieve block are delayed until the end of the scope.

The compiler will put out code for x86, Ageia PhysX and Cell/PS3.

* Side-effects are defined to be any modifications to data declared outwith the sieve block scope.

* Only functions annotated with sieve or immediate can be called.

Delaying side-effects removes many small dependencies which would usually impede automatic parallelization. Reads and writes can be safely reordered by the compiler as to allow better use of various data movement mechanisms, such as [[Direct Memory Access]](DMA). In addition, [[alias analysis]] and [[dataflow analysis]] can be simplified[http://www.cl.cam.ac.uk/~al407/research/papers/eupar07.pdf]. The compiler can then split up code within the sieve block much easier, to exploit parallelism.

==Memory Configuration==

This separation of scopes also means the Sieve System can be used in non-uniform memory architectures. Multi-core CPUs such as the [[Cell microprocessor]] used in the [[Playstation 3]] are of this type, in which the fast cores have local memories that must be utilized to exploit performance inherent in the system. It is also able to work on shared memory systems, like x86, meaning it can run on various different architectures. Sieve blocks can also be nested[http://www.cs.cmu.edu/~damp/finalPapers/lindley.pdf] for systems with a hierarchy of different memories and processing elements.

==Parallelization and Scalability==

The sieve compiler can split code within a sieve block into chunks either implicitly or explicitly though a 'splithere' statement. For instance, the following example shows parallelizaing a loop:

sieve

{

for (iterator i(0); i<length; ++i)

{

R[i] = A[i] * B[i]

splithere;

}

}

The compiler will implicitly add a splitpoint above the for loop construct body, as an entry point. Similarly one will be added after as an exit point.

In the Sieve System, only local variables to the sieve block scope may have dependencies. However, these dependencies must not cross splitpoints; they will generate compiler warnings(cite). In order to parallelize this loop, a special 'Iterator' class may be be used in place of a standard integer looping counter. There are safe for parallelization, and the programmer is free to create new Iterator classes at will[http://codeplaysoftware.typepad.com/codeplay/2007/05/loop_carried_de.html]. In addition to these Iterator classes, the programmer is free to implement classes called 'Accumulators' which are used to carry out reduction operations.

The way the Iterator classes are implemented opens up various means for scalability. The Sieve Parallel Runtime employs dynamic [[speculative execution]] when executing on a target platform. This can yield very good speedups, however running on a single core machine can incur overheads[http://www.codeplay.com/technology/sievebenchmarks.html].

==Determinism==

Determinism is an interesting feature of the Sieve System. If executing a parallel Sieve program on a multi core machine yields a bug, the bug will not dissapear when ran on a single core to aid [[debugging]][http://www.codeplay.com/downloads_public/sievepaper-2columns-normal.pdf][http://www.cl.cam.ac.uk/~al407/research/papers/eupar07.pdf]. This is a huge step forward for parallel computer software, as it eliminates [[Race Conditions]] which are one of the most common bugs to arise from [[concurrent programming]]. The removal of the need to consider [[concurrency control]] structures within a sieve block can speed up development time and results in safer code.

==Supported Systems==

The system is designed for hierarchical based systems with homogeneous or heterogeneous CPU cores which have local memories, connected via DMA engines or similar memory transfer models.

Sieve has been shown[http://www.codeplay.com/technology/sievebenchmarks.html] successfully operating on multi-core x86 systems, the [[Ageia]] [[PhysX]] [[Physics Processing Unit]], and the IBM [[Cell microprocessor]]. [[ANSI C]] is generated if a compiler [[code generator]] is not available for a certain target platform. This allows for autoparallelization using existing C compilation toolkits [http://mgrid.feis.herts.ac.uk/wp-content/scott.ppt].

==References==

*[http://www.cl.cam.ac.uk/~al407/research/papers/hppc07.pdf Auto-parallelisation of Sieve C++ Programs] Alastair Donaldson, Anton Lokhmotov, Colin Riley, Andrew Cook. In Proceedings of the Euro-Par Workshop Highly Parallel Processing on a Chip (HPPC'07), Rennes, France, August 2007. Lecture Notes in Computer Science 4854, 2007.

*[http://www.codeplay.com.nyud.net:8090/customcompilers/multi-core.html Codeplay.com]

*[http://www.cl.cam.ac.uk/~al407/research/papers/eupar07.pdf Delayed Side-effects Ease Multi-core Programming] Anton Lokhmotov, Alan Mycroft, Andrew Richards. In Proceedings of the 13th International Euro-Par Conference, Rennes, France, August 2007. Lecture Notes in Computer Science 4641, 641-650, 2007.

*[http://72.14.209.104/search?q=cache:http://www.codeplay.com/customcompilers/multi-core.html]

*[http://www.cs.cmu.edu/%7Edamp~damp/finalPapers/lindley.pdf WhiteImplementing Paperdeterministic declarative conucrrency using sieves] -S. Lindley. In proceedings of DAMP 2007: Workshop on SieveDeclarative Aspects of Multicore Programming Nice, France, January 2007.

*[http://www.codeplay.com/downloads_public/sievepaper-2columns-normal.pdf The Codeplay Sieve C++ Parallel Programming System] A. Richards. White paper, 2006.

==External links==

*[http://mgrid.feis.herts.ac.uk/wp-content/scott.ppt Codeplay Sieve C++ System Presentation] Scott McKenzie, presented at [http://mgrid.feis.herts.ac.uk/ MicroGrid 2006].

*[http://www.codeplay.com/ Codeplay]

*[http://sourceforge.net/projects/rpa Range Partition Adaptors]: Multi-threaded STL algorithms.

==See Also==

*[[Software transactional memory]]

*[[Alias Analysis]]

*[[OpenMP]]

*[[Intel Threading Building Blocks]]

*[[Cilk]]

*[[Speculative execution]]

==External linksLinks==

*[http://www.codeplay.com.nyud.net:8090/customcompilerstechnology/multi-coresieve.html Codeplay.com Sieve Website]

{{Parallel Computing}}

[[Category:C programming language family]]

[[Category:Parallel computing]]

[[Category:Application programming interfaces]]

[[Category:C programming language family]]

[[Category:C++ programming language family]]

[[Category:C++ standard library]]

{{Compu-stub}}

[[Category:Concurrent computing]]

[[Category:Multicore Processors]]

[[Category:Programming paradigms]]