Mapping performance data for high-level and data views of parallel program performance
ICS '96 Proceedings of the 10th international conference on Supercomputing
Performance measurement tools for high-level parallel programming languages
Performance measurement tools for high-level parallel programming languages
On the complexity of escape analysis
Proceedings of the 24th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
A sound type system for secure flow analysis
Journal of Computer Security
Tools for application-oriented performance tuning
ICS '01 Proceedings of the 15th international conference on Supercomputing
SvPablo: A Multi-language Performance Analysis System
TOOLS '98 Proceedings of the 10th International Conference on Computer Performance Evaluation: Modelling Techniques and Tools
Gprof: A call graph execution profiler
SIGPLAN '82 Proceedings of the 1982 SIGPLAN symposium on Compiler construction
The role of instrumentation and mapping in performance measurement
The role of instrumentation and mapping in performance measurement
LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
The Tau Parallel Performance System
International Journal of High Performance Computing Applications
Dynamic inference of abstract types
Proceedings of the 2006 international symposium on Software testing and analysis
Quantitative information flow as network flow capacity
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Data centric techniques for mapping performance data to program variables
Parallel Computing
Pinpointing data locality problems using data-centric analysis
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
Decision support via automated metric comparison for the palladio-based performance blame analysis
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
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Parallel programs are increasingly being written using programming frameworks and other environments that allow parallel constructs to be programmed with greater ease. The data structures used allow the modeling of complex mathematical structures like linear systems and partial differential equations using high-level programming abstractions. While this allows programmers to model complex systems in a more intuitive way, it also makes the debugging and profiling of these systems more difficult due to the complexity of mapping these high level abstractions down to the low level parallel programming constructs. This work discusses mapping mechanisms, called variable blame, for creating these mappings and using them to assist in the profiling and debugging of programs created using advanced parallel programming techniques. We also include an example of a prototype implementation of the system profiling three programs.