Exploiting spatial architectures for edit distance algorithms

In this paper, we demonstrate the ability of spatial architectures to significantly improve both runtime performance and energy efficiency on edit distance, a broadly used dynamic programming algorithm. Spatial architectures are an emerging class of application accelerators that consist of a network of many small and efficient processing elements that can be exploited by a large domain of applications. In this paper, we utilize the dataflow characteristics and inherent pipeline parallelism within the edit distance algorithm to develop efficient and scalable implementations on a previously proposed spatial accelerator. We evaluate our edit distance implementations using a cycle-accurate performance and physical design model of a previously proposed triggered instruction-based spatial architecture in order to compare against real performance and power measurements on an x86 processor. We show that when chip area is normalized between the two platforms, it is possible to get more than a 50× runtime performance improvement and over 100× reduction in energy consumption compared to an optimized and vectorized x86 implementation. This dramatic improvement comes from leveraging the massive parallelism available in spatial architectures and from the dramatic reduction of expensive memory accesses through conversion to relatively inexpensive local communication.