Execution and Cache Performance of the Scheduled Dataflow Architecture

This paper presents an evaluation of our Scheduled Dataflow (SDF) Processor. Recent focus in the field of new processor architectures is mainly on VLIW (e.g. IA-64), superscalar and superspeculative architectures. This trend allows for better performance at the expense of an increased hardware complexity and a brute-force solution to the memory-wall problem. Our research substantially deviates from this trend by exploring a simpler, yet powerful execution paradigm that is based on dataflow concepts. A program is partitioned into functional execution threads, which are perfectly suited for our non-blocking multithreaded architecture. In addition, all memory accesses are decoupled from the thread's execution. Data is pre-loaded into the thread's context (registers), and all results are post-stored after the completion of the thread's execution. The decoupling of memory accesses from thread execution requires a separate unit to perform the necessary pre-loads and post-stores, and to control the allocation of hardware thread contexts to enabled threads. The analytical analysis of our architecture showed that we could achieve a better performance than other classical dataflow architectures (i.e., ETS), hybrid models (e.g., EARTH) and decoupled multithreaded architectures (e.g., Rhamma processor). This paper analyzes the architecture using an instruction set level simulator for a variety of benchmark programs. We compared the execution cycles required for programs on SDF with the execution cycles required by the programs on DLX (or MIPS). Then we investigated the expected cache-memory performance by collecting address traces from programs and using a trace-driven cache simulator (Dinero-IV). We present these results in this paper.