Wish Branches: Combining Conditional Branching and Predication for Adaptive Predicated Execution
Proceedings of the 38th annual IEEE/ACM International Symposium on Microarchitecture
Identifying potential parallelism via loop-centric profiling
Proceedings of the 4th international conference on Computing frontiers
Mostly static program partitioning of binary executables
ACM Transactions on Programming Languages and Systems (TOPLAS)
MediaBench II video: Expediting the next generation of video systems research
Microprocessors & Microsystems
A hardware hot loop path detector for dynamic parallelization and optimization
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
LPA: a first approach to the loop processor architecture
HiPEAC'08 Proceedings of the 3rd international conference on High performance embedded architectures and compilers
Identifying hotspots in a program for data parallel architecture: an early experience
Proceedings of the 5th India Software Engineering Conference
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To obtain the benefits of aggressive, wide-issue, architectures, a large window of valid instructions must be available. While researchers have been successful in obtaining high accuracies with a range of dynamic branch predictors, there still remains the need for more aggressive instruction delivery. Loop bodies possess a large amount of spatial and temporal locality. A large percentage of a program's entire execution can be attributed to code found in loop bodies. If we retain this code in a buffer or the cache, we do not have to refetch this code on subsequent loop iterations., Loops tend to iterate multiple times before exiting, thus providing us with the opportunity to speculatively issue multiple iterations. While some loops can be unrolled by a compiler many contain conditional branches. The number of times a loop iterates may be dependent on a program variable. These issues can hinder our ability to speculatively issue multiple iterations of a loop. If we are able to profile loops during runtime, we can use this information to more accurately issue speculative paths through loop bodies. In this paper we present a characterization of loop execution across the SPECint2000 benchmark suite. We intend for this study to serve as a guide in the selection of design parameters of a loop path predictor We characterize the patterns exhibited during multiple visits to a loop body. We present the design of a table that records path-based loop execution history and allows us to predict multiple loop iterations dynamically.