PhD Position F/M Optimization of high-performance applications on heterogeneous computing nodes
A PhD position is open in HiePACS, a joint project-team with Bordeaux INP, Bordeaux University and CNRS, and CAMUS, a joint project-team with Strasbourg University and CNRS.
The purpose of the HiePACS project is to efficiently perform frontier simulations arising from challenging research and industrial multiscale applications. The solution of these challenging problems requires a multidisciplinary approach involving applied mathematics, computational and computer sciences. In applied mathematics, it essentially involves advanced numerical schemes. In computational science, it involves massively parallel computing and the design of highly scalable algorithms and codes to be executed on future petaflop (and beyond) platforms. Through this approach, HiePACS intends to contribute to all steps that go from the design of new high-performance more scalable, robust and more accurate numerical schemes to the optimized implementations of the associated algorithms and codes on very high performance supercomputers.
The CAMUS research team focuses on parallelization, optimization, profiling, modeling, and compilation. The team has increasing interests in the approaches used and enhanced in the high-performance community. The team’s research activities are organized into five main issues that are closely related to reach the following objectives: performance, correction and productivity. These issues are: static parallelization and optimization of programs (where all statically detected parallelisms are expressed as well as all “hypothetical” parallelisms which would be eventually taken advantage of at runtime), profiling and execution behavior modeling (where expressive representation models of the program execution behavior will be used as engines for dynamic parallelizing processes), dynamic parallelization and optimization of programs (such transformation processes running inside a virtual machine), object-oriented programming and compiling for multicores (where object parallelism, expressed or detected, has to result in efficient runs), and finally program transformations proof (where the correction of many static and dynamic program transformations has to be ensured).
The objectives of the thesis will be to study how the new features of the TEXTAROSSA computing nodes can be used to develop high performance applications. With this aim, we will study how high performance task-based applications can be adapted in order to exploit the full potential of the platform. We will thus consider two existing high performance libraries by adapting them, designing advanced scheduling strategies and considering energy consumption awareness as a major constraint of the work.