As multicore processors are deployed in mainstream computing, the need for software tools to help parallelize programs is increasing dramatically. Data-dependence profiling is an important technique to exploit parallelism in programs. More specifically, manual or automatic parallelization can use the outcomes of data-dependence profiling to guide where to parallelize in a program. However, state-of-the-art data-dependence profiling techniques are not scalable as they suffer from two major issues when profiling large and long-running applications: (1) runtime overhead and (2) memory overhead. Existing data-dependence profilers are either unable to profile large-scale applications or only report very limited information. In this paper, ...
The popularity of parallel systems for building high performance software only continues to rise. Pr...
Data dependence analysis techniques are the main component of today's strategies for automatic ...
Performance analysis of parallel programs continues to be challenging for programmers. Programmers h...
International audienceThis paper describes a tool using one or more executions of a sequential progr...
International audienceAlthough parallel processing is mainstream, existing programs are often serial...
Workload consolidation is a common method to increase resource utilization of the clusters or data c...
Traditional static analysis fails to auto-parallelize programs with a complex control and data flow....
While the chip multiprocessor (CMP) has quickly become the predominant processor architecture, its c...
The emergence of multicore processors has increased the need for simple parallel programming models ...
Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 2012.Speculative parallelizatio...
With the proliferation of multicore processors, there is an urgent need for tools and methodologies ...
Parallelization is a technique that boosts the performance of a program beyond optimizations of the ...
In the era of multicore processors, the responsibility for performance gains has been shifted onto s...
Recent trends show a steady increase in the utilization of heterogeneous multicore architectures in ...
Over the past 10 years we have seen the transition from single core computer to multicore computing,...
The popularity of parallel systems for building high performance software only continues to rise. Pr...
Data dependence analysis techniques are the main component of today's strategies for automatic ...
Performance analysis of parallel programs continues to be challenging for programmers. Programmers h...
International audienceThis paper describes a tool using one or more executions of a sequential progr...
International audienceAlthough parallel processing is mainstream, existing programs are often serial...
Workload consolidation is a common method to increase resource utilization of the clusters or data c...
Traditional static analysis fails to auto-parallelize programs with a complex control and data flow....
While the chip multiprocessor (CMP) has quickly become the predominant processor architecture, its c...
The emergence of multicore processors has increased the need for simple parallel programming models ...
Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 2012.Speculative parallelizatio...
With the proliferation of multicore processors, there is an urgent need for tools and methodologies ...
Parallelization is a technique that boosts the performance of a program beyond optimizations of the ...
In the era of multicore processors, the responsibility for performance gains has been shifted onto s...
Recent trends show a steady increase in the utilization of heterogeneous multicore architectures in ...
Over the past 10 years we have seen the transition from single core computer to multicore computing,...
The popularity of parallel systems for building high performance software only continues to rise. Pr...
Data dependence analysis techniques are the main component of today's strategies for automatic ...
Performance analysis of parallel programs continues to be challenging for programmers. Programmers h...