Safe programmable speculative parallelism
PLDI '10 Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation
HotPar'09 Proceedings of the First USENIX conference on Hot topics in parallelism
Practical speculative parallelization of variable-length decompression algorithms
Proceedings of the 14th ACM SIGPLAN/SIGBED conference on Languages, compilers and tools for embedded systems
HPar: A practical parallel parser for HTML--taming HTML complexities for parallel parsing
ACM Transactions on Architecture and Code Optimization (TACO)
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Software speculative parallelization has shown effectiveness in parallelizing certain applications. Prior techniques have mainly relied on simple exploitation of heuristics for speculation. In this work, we introduce probabilistic analysis into the design of speculation schemes. In particular, by tackling applications that are based on Finite State Machine (FSM) which have the most prevalent dependences among all programs, we show that the obstacles for effective speculation can be much better handled with rigor. We develop a probabilistic model to formulate the relations between speculative executions and the properties of the target computation and inputs. Based on the formulation, we propose two model-based speculation schemes that automatically customize themselves with the best configurations for a given FSM and its inputs. The new technique produces substantial speedup over the state of the art.