Abstract
The Dynamically Partitioned Data-Flow (DPDF) model is based on an original analysis concept of the data dependency graph at the instruction level. Instead of a breadth first analysis, as in a classical Data-Flow Model, we execute instructions along data-dependent paths. As a consequence, data locality can be exploited by reusing results between the execution of consecutive instructions. In addition, the different paths are not statically defined but arise from a dynamical partitioning of the graph. This model presents the advantage to support very small cost dynamic scheduling and multitasking strategies. In order to study the efficiency of this new model, a first architecture has been defined. This architecture is currently limited to a single processor with one serial processing unit but four graph analyzing units (called prefetch units). Each of these prefetch units is able to build dynamically its own execution path inside the Data-Flow graph of an application. The efficiency of this architecture is studied on a numerical benchmark composed of a subset of the Livermore loops and of three routines of the Level 3 BLAS (GEMM, SYRK and TRSM). Our goal in these experimentations is to demonstrate the ability of the four prefetch units to feed the ALU.
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