PARALLEL LOW-OVERHEAD DATA COLLECTION FRAMEWORK FOR A RESOURCE CENTRIC PERFORMANCE ANALYSIS TOOL
Sunil Shrestha · Library, Museums and Press - UDSpace (University of Delaware) · 2012
With the advent of multicore technology, computer systems have shifted to a new height of parallelism and computational power. Ramping up the frequency to increase performance on single processor has become a thing of the past. Nowadays, everyday computers are powered by multiple cores that share resources such as memory, network and I/O components. Moreover, they can run a larger gamut of applications at much higher speed. The increase in computational power is not reflected in the usability of such systems. The usage of these components and resources in an effective manner puts an extremely high burden to the programmer and the system software, which increases the complexity in programming models and runtime systems. To alleviate this burden, there is a need for tools that can identify parallel sections in the code, identify bottlenecks and provides hint to the programmers to improve the performance of the overall computing system. Moreover, the tool has to be able to pinpoint resource contentions so we know where uneven distributions of resources are. To address this issue, we introduced a tool called Memory Observant Data Analysis (MODA) [44]. MODA is a performance analysis tool that helps users analyze resource usage and alleviate resource conflicts by pinpointing performance issues in an algorithmic as well as in an architectural level. The main challenge of any performance analysis tool is the tool performance itself. Performance analysis tool needs to be such that it introduces minimal to no perturbation of application behavior. This requires analysis tool to achieve information during runtime with a very minimal overhead. This is not easy to achieve because