Towards automatic support of parallel sparse computation in Java with continuous compilation

[[abstract]]We present a generic matrix class facility in Java and an on-going project for a runtime environment with continuous compilation aiming to support automatic parallelization of sparse computation on distributed environments. Our package comes with a collection of matrix classes with a uniform interface for operations on dense and sparse matrices. These matrix operations are implemented both for sequential and parallel executions on distributed memory environments. In our environment, a program such as the conjugate gradient solver is written by users using high-level generic matrix notations in Java. At runtime the generic notations are mapped to specific implementations. Our approach is particularly useful for optimizing sparse computation for distributed environments because, with the help of profiling information and a cost model, it can automatically select suitable compression and distribution schemes according to access patterns of the programs and non-zero structures of the matrices. Our testbed is currently based on Java and PVM on an IBM SP2 workstation cluster. Preliminary experimental results show that our approach is promising in speeding up sparse matrix computations on distributed memory environments.[[fileno]]2030216010001[[department]]資訊工程學