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December 5, 2016
The committee will be composed of:
- Pr, Martin Quinson, ENS Rennes, President
- Pr, Jesus Labarta Mancho, Universitat Politècnica de Catalunya, Reviewer
- Pr, Raymond Namyst, Université de Bordeaux, Reviewer
- Dr, Lucas M. Schnorr, Universidade Federal do Rio Grande Do Sul, Examiner
- Pr, Bruno Raffin, Inria Grenoble, Advisor
- Dr Guillaume Huard, University of Grenoble Alpes, Co-Advisor
Abstract:
Since a few decades, to reduce energy consumption, processor vendors
builds more and more parallel computers. At the same time, the gap
between processors and memory frequency increased significantly. To
mitigate this gap, processors embed a complex hierarchical caches
architecture. Writing efficient code for such computers is a complex
task. Therefore, performance analysis has became an important step of
the development of applications performing heavy computations.
Most existing performance analysis tools focuses on the point of view
of the processor. Theses tools see the main memory as a monolithic
entity and thus are not able to understand how it is accessed.
However, memory is a common bottleneck in HPC, and the pattern of
memory accesses can impact significantly the performances. There are
a few tools to analyze memory performances, however theses tools are
based on a coarse grain sampling. Consequently, they focus on a small
part of the execution missing the global memory behavior.
Furthermore, these coarse grain sampling are not able to collect
memory accesses patterns.
In this thesis we propose two different tools to analyze the memory
behavior of an application. The first tool is designed specifically
for NUMA machines and provides some visualizations of the global
sharing pattern inside each data structure between the threads. The
second one collects fine grain memory traces with temporal
information. We can visualize theses traces either with a generic
trace management framework or with a programmatic exploration using R.
Furthermore we evaluate both of these tools, comparing them with state
of the art memory analysis tools in terms of performances, precision
and completeness.
