Rapid Parallelization by Collaboration

The widespread adoption of Chip Multiprocessors has renewed the emphasis on the use of parallelism to improve performance. The present and growing diversity in hardware architectures and software environments, however, continues to pose difficulties in the effective use of parallelism thus delaying a quick and smooth transition to the concurrency era. In this document, we describe the research being conducted at the Computer Science Department at Columbia University on a system called COMPASS that aims to simplify this transition by providing advice to programmers considering parallelizing their code. The advice proffered to the programmer is based on the wisdom collected from programmers who have already parallelized some code. The utility of COMPASS rests, not only on its ability to collect the wisdom unintrusively but also on its ability to automatically seek, find and synthesize this wisdom into advice that is tailored to the code the user is considering parallelizing and to the environment in which the optimized program will execute in. COMPASS provides a platform and an extensible framework for sharing human expertise about code parallelization -- widely and on diverse hardware and software. By leveraging the "Wisdom of Crowds" model which has been conjunctured to scale exponentially and which has successfully worked for Wikis, COMPASS aims to enable rapid parallelization of code and thus continue to extend the benefits for Moore's law scaling to science and society

Limits of Instruction Level Parallelism with Data Value Speculation

. Increasing the instruction level parallelism (ILP) is one of the key issues to boost the performance of future generation processors. Current processor organizations include different mechanisms to overcome the limitations imposed by name and control dependences but no mechanisms targeting to data dependences. Thus, these dependences will become one of the main bottlenecks in the future. Data value speculation is gaining popularity as a mechanism to overcome the limitations imposed by data dependences by predicting the values that flow through them. In this work, we present a study of the potential of data value speculation to boost the limits of instruction level parallelism using both perfect and realistic predictors. Speedups obtained by data value speculation are very huge for an infinite window and still significant for a limited window. Different prediction schemes oriented to single thread and multiple threads (from a single program) architectures have been studied. The latter..