Abstract
We describe a programming framework for high performance clusters with various hardware accelerators. In this framework, users can utilize the available heterogeneous resources productively and efficiently. The distributed application is highly modularized to support dynamic system configuration with changing types and number of the accelerators. Multiple layers of communication interface are introduced to reduce the overhead in both control messages and data transfers. Parallelism can be achieved by controlling the accelerators in various schemes through scheduling extension. The framework has been used to support physics simulation and financial application development. We achieve significant performance improvement on a 16-node cluster with FPGA and GPU accelerators.
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