- General Chairs:
- Clemens Grelck,
- Fritz Henglein,
- Program Chairs:
- Umut Acar,
- Jost Berthold
- Sponsor:
- sigplan
It is our great pleasure to welcome you to the 2nd ACM SIGPLAN Workshop on Functional High-Performance Computing. FHPC 2013 brings together researchers who explore declarative highlevel programming technology in application domains where large-scale computations arise naturally and high performance is essential. The workshop is in its second year. Our goal is to establish FHPC as a regular annual forum for researchers interested in applying functional programming techniques in the area of high-performance computing. Functional programming is increasingly recognized as presenting a nice sweet spot between expressiveness and efficiency for parallel programming, reconciling execution performance with programming productivity.
Making FHPC'13 happen depended on a number of people and organizations, which we would like to acknowledge here. We thank the authors and panelists for providing the content of the program. We would like to express our gratitude to the program committee and the additional reviewers, who worked very hard in reviewing papers and providing suggestions for their improvements. Special thanks go to ACM SIGPLAN and the ICFP workshop chairs for accepting our workshop nomination and being flexible with organizational matters.
The call for papers attracted 14 submissions from Asia, the Americas, and Europe. An international program committee selected 8 contributions for publication. These papers cover a variety of topics. Some touch upon optimizing compilation techniques and programming techniques for GPU applications. Others propose novel parallel programming models, libraries, and bespoke runtime management, which take advantage of declarative constructs for better performance and productivity.
In addition to the refereed contributions, FHPC'13 features two invited talks. Matthew Fluet from Rochester Institute of Technology will provide an overview of the Manticore project, with focus on programming models and runtime techniques. Manuel Chakravarty from the University of New South Wales will present different strands of work in data-parallel computing, discussing results and issues in Data-Parallel Haskell and Accelerate. The topic of data-parallelism and GPU computing will be further deepened in a panel discussion.
We hope to have put together an interesting program, looking forward to stimulating discussions during the second FHPC workshop, and a successful follow-up FHPC workshop at ICFP 2014.
Proceeding Downloads
The manticore project
The Manticore project is a research effort to design and implement a parallel functional programming language that targets commodity multicore and shared-memory multiprocessors. Our language is a dialect of Standard ML, called Parallel ML (PML), that ...
ViperVM: a runtime system for parallel functional high-performance computing on heterogeneous architectures
The current trend in high-performance computing is to use heterogeneous architectures (i.e. multi-core with accelerators such as GPUs or Xeon Phi) because they offer very good performance over energy consumption ratios. Programming these architectures ...
Towards a streaming model for nested data parallelism
The language-integrated cost semantics for nested data parallelism pioneered by NESL provides an intuitive, high-level model for predicting performance and scalability of parallel algorithms with reasonable accuracy. However, this predictability, ...
Towards systematic parallel programming of graph problems via tree decomposition and tree parallelism
Many graph optimization problems, such as the Maximum Weighted Independent Set problem, are NP-hard. For large scale graphs that have billions of edges or vertices, these problems are hard to be computed directly even using popular data-intensive ...
Counting and occurrence sort for GPUs using an embedded language
This paper investigates two sorting algorithms: counting sort and a variation, occurrence sort, which also removes duplicate elements, and examines their suitability for running on the GPU. The duplicate removing variation turns out to have a natural ...
A T2 graph-reduction approach to fusion
Fusion is one of the most important code transformations as it has the potential to substantially optimize both the memory hierarchy time overhead and, sometimes asymptotically, the space requirement.
In functional languages, fusion is naturally and ...
Semantics-preserving data layout transformations for improved vectorisation
Data-Layouts that are favourable from an algorithmic perspective often are less suitable for vectorisation, i.e., for an effective use of modern processor's vector instructions. This paper presents work on a compiler driven approach towards ...
LVars: lattice-based data structures for deterministic parallelism
Programs written using a deterministic-by-construction model of parallel computation are guaranteed to always produce the same observable results, offering programmers freedom from subtle, hard-to-reproduce nondeterministic bugs that are the scourge of ...
Towards a functional run-time for dense NLA domain
We investigate the use of functional programming to develop a numerical linear algebra run-time; i.e. a framework where the solvers can be adapted easily to different contexts and task parallelism can be attained (semi-) automatically. We follow a ...
Data parallelism in Haskell
The implicit data parallelism in collective operations on aggregate data structures constitutes an attractive parallel programming model for functional languages. Beginning with our work on integrating nested data parallelism into Haskell, we explored a ...
Index Terms
- Proceedings of the 2nd ACM SIGPLAN workshop on Functional high-performance computing