Highly accurate data value prediction using hybrid predictors
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Task selection for a multiscalar processor
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Value speculation scheduling for high performance processors
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
A Chip-Multiprocessor Architecture with Speculative Multithreading
IEEE Transactions on Computers
Value prediction for speculative multithreaded architectures
Proceedings of the 32nd annual ACM/IEEE international symposium on Microarchitecture
A general compiler framework for speculative multithreading
Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures
Costs and Benefits of Multithreading with Off-the-Shelf RISC Processors
Euro-Par '95 Proceedings of the First International Euro-Par Conference on Parallel Processing
PACT '96 Proceedings of the 1996 Conference on Parallel Architectures and Compilation Techniques
A General Compiler Framework for Speculative Multithreaded Processors
IEEE Transactions on Parallel and Distributed Systems
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Speculative multithreading (SpMT) is an effective execution model for parallelizing non-numeric programs, which tend to use irregular and pointer-intensive data structures, and have complex flows of control. An SpMT compiler performs program partitioning by carefully considering the data dependencies present in the program. However, at run-time, the data dependency picture changes dramatically if the SpMT hardware performs data value prediction. Many of the data dependencies, which guided the compiler's partitioning algorithm in taking decisions, may lose their relevance due to successful data value prediction. This paper presents a compiler framework that uses profile-based value predictability information when making program partitioning decisions. We have developed a Value Predictability Profiler (VPP) that generates the value prediction statistics for the source variables in a program. Our SpMT compiler utilizes this information by ignoring the data dependencies due to variables with high prediction accuracies. The compiler can thus perform more efficient thread formation. This SpMT compiler framework is implemented on the SUIF-MachSUIF platform. A simulation-based evaluation of SPEC programs shows that the speedup with 6 processing elements increases up to 21% when utilizing value predictability information during program partitioning.