POPL '88 Proceedings of the 15th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
A data locality optimizing algorithm
PLDI '91 Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation
Tiling multidimensional iteration spaces for nonshared memory machines
Proceedings of the 1991 ACM/IEEE conference on Supercomputing
An overview of High Performance Fortran
ACM SIGPLAN Fortran Forum
Co-array Fortran for parallel programming
ACM SIGPLAN Fortran Forum
Organizing matrices and matrix operations for paged memory systems
Communications of the ACM
X10: an object-oriented approach to non-uniform cluster computing
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Programming for parallelism and locality with hierarchically tiled arrays
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
A parallel hybrid banded system solver: the SPIKE algorithm
Parallel Computing - Parallel matrix algorithms and applications (PMAA'04)
Sequoia: programming the memory hierarchy
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Sequoia: programming the memory hierarchy
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Parallel Programmability and the Chapel Language
International Journal of High Performance Computing Applications
On the Performance Enhancement of Paging Systems Through Program Analysis and Transformations
IEEE Transactions on Computers
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Task-Parallel versus Data-Parallel Library-Based Programming in Multicore Systems
PDP '09 Proceedings of the 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing
Optimization techniques for efficient HTA programs
Parallel Computing
| Hi-index | 0.00 |
Solving linear systems is an important problem for scientific computing. Exploiting parallelism is essential for solving complex systems, and this traditionally involves writing parallel algorithms on top of a library such as MPI. The SPIKE family of algorithms is one well-known example of a parallel solver for linear systems. The Hierarchically Tiled Array data type extends traditional data-parallel array operations with explicit tiling and allows programmers to directly manipulate tiles. The tiles of the HTA data type map naturally to the block nature of many numeric computations, including the SPIKE family of algorithms. The higher level of abstraction of the HTA enables the same program to be portable across different platforms. Current implementations target both shared-memory and distributed-memory models. In this paper we present a proof-of-concept for portable linear solvers. We implement two algorithms from the SPIKE family using the HTA library. We show that our implementations of SPIKE exploit the abstractions provided by the HTA to produce a compact, clean code that can run on both shared-memory and distributed-memory models without modification. We discuss how we map the algorithms to HTA programs as well as examine their performance. We compare the performance of our HTA codes to comparable codes written in MPI as well as current state-of-the-art linear algebra routines.