Lazy Parallelization: A Finite State Machine Based Optimization Approach for Data Parallel Image Processing Applications

Performance obtained with existing library-based parallelization tools for implementing high performance image processing applications is often sub-optimal. This is because inter-operation optimization (or: optimization across library calls) is often not incorporated in the library implementations. This paper presents a simple, efficient, finite state machine-based method for global performance optimization, called ’lazy parallelization’. Experimental results based on this approach show significant performance improvements over non-optimized parallel implementations.

• Type: Conference
• Year: 2003
• Where: IPPS
• Authors: Frank J. Seinstra, Dennis Koelma
• Keywords: Distributed And Parallel Computing, Global Performance Optimization, IPPS 2003, Library-based Parallelization Tools, Performance Image Processing