An easy-to-use toolkit for efficient Java bytecode translators
Proceedings of the 2nd international conference on Generative programming and component engineering
Dynamic selection of application-specific garbage collectors
Proceedings of the 4th international symposium on Memory management
Optimistic parallelism requires abstractions
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Adaptive transaction scheduling for transactional memory systems
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
Committing conflicting transactions in an STM
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
Dependence-aware transactional memory for increased concurrency
Proceedings of the 41st annual IEEE/ACM International Symposium on Microarchitecture
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
Cross-Input Learning and Discriminative Prediction in Evolvable Virtual Machines
Proceedings of the 7th annual IEEE/ACM International Symposium on Code Generation and Optimization
Preventing versus curing: avoiding conflicts in transactional memories
Proceedings of the 28th ACM symposium on Principles of distributed computing
Taking the heat off transactions: Dynamic selection of pessimistic concurrency control
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Adaptive Locks: Combining Transactions and Locks for Efficient Concurrency
PACT '09 Proceedings of the 2009 18th International Conference on Parallel Architectures and Compilation Techniques
Speculation with Little Wasting: Saving Cost in Software Speculation through Transparent Learning
ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
Exploiting statistical correlations for proactive prediction of program behaviors
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Lightweight, robust adaptivity for software transactional memory
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
An input-centric paradigm for program dynamic optimizations
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
The tao of parallelism in algorithms
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
Performance evaluation of adaptivity in software transactional memory
ISPASS '11 Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software
HAWKEYE: effective discovery of dataflow impediments to parallelization
Proceedings of the 2011 ACM international conference on Object oriented programming systems languages and applications
A transactional memory with automatic performance tuning
ACM Transactions on Architecture and Code Optimization (TACO) - HIPEAC Papers
POPL '12 Proceedings of the 39th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Conflict detection and validation strategies for software transactional memory
DISC'06 Proceedings of the 20th international conference on Distributed Computing
JANUS: exploiting parallelism via hindsight
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Concurrent data representation synthesis
Proceedings of the 33rd ACM SIGPLAN conference on Programming Language Design and Implementation
Sambamba: a runtime system for online adaptive parallelization
CC'12 Proceedings of the 21st international conference on Compiler Construction
Thread reinforcer: Dynamically determining number of threads via OS level monitoring
IISWC '11 Proceedings of the 2011 IEEE International Symposium on Workload Characterization
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Irregular applications often exhibit data-dependent parallelism: Different inputs, and sometimes also different execution phases, enable different levels of parallelism. These changes in available parallelism have motivated work on adaptive concurrency control mechanisms. Existing adaptation techniques mostly learn about available parallelism indirectly, through runtime monitors that detect pathologies (e.g. excessive retries in speculation or high lock contention in mutual exclusion). We present a novel approach to adaptive parallelization, whereby the effective level of parallelism is predicted directly based on input features, rather than through circumstantial indicators over the execution environment (such as retry rate). This enables adaptation with foresight, based on the input data and not the run prefix. For this, the user specifies input features, which our system then correlates with the amount of available parallelism through offline learning. The resulting prediction rule serves in deployment runs to foresee the available parallelism for a given workload and tune the parallelization system accordingly. We have implemented our approach in TIGHTFIT, a general framework for input-centric offline adaptation. Our experimental evaluation of TIGHTFIT over two adaptive runtime systems and eight benchmarks provides positive evidence regarding TIGHTFIT's efficacy and accuracy.