Summary and conclusions.

allel with computation. This optimization can be done only when permissible under data dependency constraints, but has significant potential for blocked linear algebra codes. 8 Summary and Conclusions We believe that this paper has described a promising direction in automatic generation of efficient parallel implementations of linear algebra programs. This new direction is made possible by recent advances in several research areas. Blocked programs in LAPACK, systolic algorithms for frequently used block operations, and the fine-grain parallel architecture of iWarp are the important advances that paved the way for this research. We have presented a data distribution scheme and discussed how it can be used to effectively implement blocked linear algebra algorithms on distributed memory machines. We have discussed several details of implementation and demonstrated that good performance can be achieved on iWarp using our methodology. This is an ongoing research project and we are currently working on two main aspects of it. We are developing parallel implementations of several routines from LAPACK to demonstrate that the approach works for a variety of algorithms. We are also in the process of developing a tool for automatic paral-lelization of LAPACK routines on iWarp. We expect to report further on the project in future publications.