Haskell is a functional language featuring lazy evaluation and referential transparency. On one hand, Referential transparency is useful for parallel computing because the results do not depend on the evaluation order, but on the other hand, parallel computing requires an evaluation order that is different from that of lazy evaluation. There are some parallel programming libraries for Haskell, such as Repa (regular parallel arrays) and Accelerate. However, little research has been conducted on evaluation with real applications, and the usefulness of these libraries remains unclear. In this study, we evaluated the usefulness of parallel programming libraries for Haskell with an application that applies a super-resolution technique to fMRI images. We developed a CPU-based parallel program with Repa and GPU-based parallel program with Accelerate and compared their performance. We obtained reasonable speedups for the program with Repa, but not for the program with Accelerate. We also investigated Accelerate's performance issues with an implementation in C and CUDA and the log from the Accelerate program. In this paper, we report our findings through a case study, focusing on the advantages and difficulties in parallel program development with Haskell.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.25(2017) (online)------------------------------

Haskell is a functional language featuring lazy evaluation and referential transparency. On one hand, Referential transparency is useful for parallel computing because the results do not depend on the evaluation order, but on the other hand, parallel computing requires an evaluation order that is different from that of lazy evaluation. There are some parallel programming libraries for Haskell, such as Repa (regular parallel arrays) and Accelerate. However, little research has been conducted on evaluation with real applications, and the usefulness of these libraries remains unclear. In this study, we evaluated the usefulness of parallel programming libraries for Haskell with an application that applies a super-resolution technique to fMRI images. We developed a CPU-based parallel program with Repa and GPU-based parallel program with Accelerate and compared their performance. We obtained reasonable speedups for the program with Repa, but not for the program with Accelerate. We also investigated Accelerate's performance issues with an implementation in C and CUDA and the log from the Accelerate program. In this paper, we report our findings through a case study, focusing on the advantages and difficulties in parallel program development with Haskell.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.25(2017) (online)------------------------------