Abstract
The Generic Reusable Parallel Pattern Interface (GrPPI) is a very useful abstraction over different parallel pattern libraries, allowing the programmer to write generic patterned parallel code that can easily be compiled to different backends such as FastFlow, OpenMP, Intel TBB and C++ threads. However, rewriting legacy code to use GrPPI still involves code transformations that can be highly non-trivial, especially for programmers who are not experts in parallelism. This paper describes software refactorings to semi-automatically introduce instances of GrPPI patterns into sequential C++ code, as well as safety checking static analysis mechanisms which verify that introducing patterns into the code does not introduce concurrency-related bugs such as race conditions. We demonstrate the refactorings and safety-checking mechanisms on four simple benchmark applications, showing that we are able to obtain, with little effort, GrPPI-based parallel versions that accomplish good speedups (comparable to those of manually-produced parallel versions) using different pattern backends.
Highlights
The scale of parallelism in modern hardware systems is increasing at a very fast rate, with 72-core systems available off-the-shelf even in the embedded market.1 At the same time, such systems are becoming increasingly heterogeneous, integrating GPUs, FPGAs, DSLs and other specialised processors within the same chip
We present an evaluation of the refactorings to introduce Generic Reusable Parallel Pattern Interface (GrPPI) patterns into sequential code
These versions have been written by hand using C++ threads and are highly optimised
Summary
The scale of parallelism in modern hardware systems is increasing at a very fast rate, with 72-core systems available off-the-shelf even in the embedded market. At the same time, such systems are becoming increasingly heterogeneous, integrating GPUs, FPGAs, DSLs and other specialised processors within the same chip. The large scale of parallelism and heterogeneity of systems make programming modern parallel hardware very difficult, often requiring a combination of different Parallel patterns [4] have been recognised as an excellent compromise between the ease of programming and the ability to generate efficient code for large-scale heterogeneous parallel architectures. They have been endorsed by several major IT companies, such as Intel [42] and Microsoft [14], giving rise to a multitude of parallel pattern libraries, most of which are incompatible with one another and each of which usually has specific advantages (and disadvantages) over the others. An equivalent version of the code in Listing 2 is given in Listing 12
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