Francieli Zanon Boito (postdoc dans l’équipe Inria Corse à Grenoble) vient nous parler de ses travaux de recherche.

Title: I/O scheduling for HPC: finding the right access pattern and mitigating interference

Abstract: Scientific applications are executed in a high performance computing (HPC) environment, where a parallel file system (PFS) provides access to a shared storage infrastructure. The key characteristic of these systems is the use of multiple storage servers, from where data can be obtained by the clients in parallel. The performance observed by applications when accessing a PFS is directly affected by the way they perform this access, i.e. their access pattern. Additionally, when multiple applications concurrently access the PFS, their performance will suffer from interference.

In this seminar, I’ll discuss my previous and current work with I/O scheduling at different levels of the I/O stack, adapting policies to applications’ access patterns and working to mitigate interference

Title: High Performance Data Analysis for Parallel Numerical Simulations.

Author: Bruno Raffin, Director of Research, DataMove Team, Inria Grenoble

Abstract:
Large scale numerical simulations are producing an ever growing amount of data that include the simulation results as well as execution traces and logs. These data represent a double challenge.
First, these amounts of data are becoming increasingly difficult to analyse relying on traditional tools. Next, moving these data from the simulation to disks, to latter retrieve them from disks to the analysis machine is becoming increasingly costly in term of time and energy.
And this situation is expected to worsen as supercomputer I/Os and more generally data movements capabilities are progressing more slowly than compute capabilities. While the simulation was at the
center of all attentions, it is now time to focus on high performance data analysis.
This integration of data analytics with large scale simulations represents a new kind of workflow that needs adapted software solutions.

Amelie Zhou, postdoc in the Ascola research team will talk about On Achieving Efficient Data Transfer for Graph Processing in Geo-Distributed Datacenters.

Graph partitioning, which distributes graph processing workloads to multiple machines for better parallelism, is an important problem for optimizing the performance and communication cost of graph processing jobs. Recently, many graph applications such as social networks store their data on geo-distributed datacenters (DCs) to ensure flexible and low-latency services. This raises new challenges to existing graph partitioning methods, due to the costly Wide Area Network (WAN) bandwidths and the heterogeneous network bandwidths in the geo-distributed DCs. In this paper, we propose a heterogeneity-aware graph partitioning method named G-Cut, which aims at minimizing the runtime of graph processing jobs in geo-distributed DCs while satisfying the WAN usage budget. G-Cut is a two-stage graph partitioning method. In the traffic-aware graph partitioning stage, we adopt the one-pass edge
assignment to place edges into different partitions while minimizing the inter-DC data traffic size. In the network-aware partition refinement stage, we map the partitions obtained in the first stage onto different
DCs in order to minimize the inter-DC data transfer time. We evaluate the effectiveness and efficiency of G-Cut using real-world graphs. The evaluation results show that G-Cut is able to obtain both lower data
transfer time and WAN usage compared to the state-of-the-art graph partitioning methods.