Post-doctoral fellow in the Research Group Parallel Computing at TU Wien
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I work on improving performance of HPC runtime systems, especially distributed runtime systems.
I did my PhD thesis in the TADaaM team in
Inria Bordeaux Sud-Ouest,
under the supervision of Alexandre Denis
and Emmanuel Jeannot.
I worked on interactions between task-based runtime systems and communication libraries for High Performance Computing, especially StarPU and NewMadeleine.
I developed two main directions in my thesis:
To be able to optimize broadcasts appearing in task graphs of
StarPU applications, we developed what we called dynamic
broadcasts. Functions such as
cannot be used within StarPU, mainly because recipient processes do
not know wheter data comes from a broadcast or a regular
point-to-point request, and in the same fashion they do not know
other nodes involved in the broadcast. Thus, only the original
sender node has all informations to be able to call
MPI_Bcast. Accurately detecting broadcasts in the task
graph is not straighforward neither.
Our dynamic broadcasts overcome these constraints. The broadcast communication pattern required by the task-based algorithm is detected automatically, then the broadcasting algorithm relies on active messages and source routing, so that participating nodes do not need to know each other and do not need to synchronize. Receiver receives data the same way as it receives point-to-point communication, without having to know it arrives through a broadcast.
To amortize the cost of MPI communications, distributed
parallel runtime systems can usually overlap network
communications with computations in the hope that it improves
global application performance. When using this technique, both
computations and communications are running at the same time.
We studied the possible interferences between computations and communications when they are executed in parallel. The main interference that can occur is memory contention between data used by computations and data used by network communications. In some cases, this contention can cause severe slowdown of both computations and communications.
To predict memory bandwidth for computations and for communications when they are executed side by side, we proposed a model taking data locality and contention into account. Elaboration of the model allowed to better understand locations of bottlenecks in the memory system and what are the strategies of the memory system in case of contention.
I defended my thesis On the Interactions between HPC Task-based Runtime Systems and Communication Libraries on November 29, 2022. The manuscript is available, the slides as well.
Authors are sorted by alphabetical order.
A complete list of my publications is available on HAL.
swartvagher [at] par [dot] tuwien [dot] ac [dot] at
I have a PGP key: 0x6EC3C10693C090C3 (942A 2C17 4547 99B5 62E3 4127 6EC3 C106 93C0 90C3).