Programming parallel algorithms
Communications of the ACM
Journal of Functional Programming
Type-safe observable sharing in Haskell
Proceedings of the 2nd ACM SIGPLAN symposium on Haskell
Nikola: embedding compiled GPU functions in Haskell
Proceedings of the third ACM Haskell symposium on Haskell
Regular, shape-polymorphic, parallel arrays in Haskell
Proceedings of the 15th ACM SIGPLAN international conference on Functional programming
Accelerating Haskell array codes with multicore GPUs
Proceedings of the sixth workshop on Declarative aspects of multicore programming
Functional and dynamic programming in the design of parallel prefix networks
Journal of Functional Programming
Intel's Array Building Blocks: A retargetable, dynamic compiler and embedded language
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Work efficient higher-order vectorisation
Proceedings of the 17th ACM SIGPLAN international conference on Functional programming
Proceedings of the 2nd ACM SIGPLAN workshop on Functional high-performance computing
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Nowadays, performance in processors is increased by adding more cores or wider vector units, or by combining accelerators like GPUs and traditional cores on a chip. Programming for these diverse architectures is a challenge. We would like to exploit all the resources at hand without putting too much burden on the programmer. Ideally, the programmer should be presented with a machine model abstracted from the specific number of cores, SIMD width or the existence of a GPU or not. Intel's Array Building Blocks (ArBB) is a system that takes on these challenges. ArBB is a language for data parallel and nested data parallel programming, embedded in C++. By offering a retargetable dynamic compilation framework, it provides vectorisation and threading to programmers without the need to write highly architecture specific code. We aim to bring the same benefits to the Haskell programmer by implementing a Haskell frontend (embedding) of the ArBB system. We call this embedding EmbArBB. We use standard Haskell embedded language procedures to provide an interface to the ArBB functionality in Haskell. EmbArBB is work in progress and does not currently support all of the ArBB functionality. Some small programming examples illustrate how the Haskell embedding is used to write programs. ArBB code is short and to the point in both C++ and Haskell. Matrix multiplication has been benchmarked in sequential C++, ArBB in C++, EmbArBB and the Repa library. The C++ and the Haskell embeddings have almost identical performance, showing that the Haskell embedding does not impose any large extra overheads. Two image processing algorithms have also been benchmarked against Repa. In these benchmarks at least, EmbArBB performance is much better than that of the Repa library, indicating that building on ArBB may be a cheap and easy approach to exploiting data parallelism in Haskell.