High-Performance Computing (HPC) platforms are
growing in size and complexity. In order to improve the quality
of service of such platforms, researchers are devoting a great
amount of effort to devise algorithms and techniques to improve
different aspects of performance such as energy consumption,
total usage of the platform, and fairness between users. In spite
of this, system administrators are always reluctant to deploy
state of the art scheduling methods and most of them revert
to EASY-backfilling, also known as EASY-FCFS (EASY-First-
Come-First-Served). Newer methods frequently are complex and
obscure and the simplicity and transparency of EASY are too
important to sacrifice.
In this work, we used execution logs from five HPC platforms
to compare four simple scheduling policies: FCFS, Shortest esti-
mated Processing time First (SPF), Smallest Requested Resources
First (SQF), and Smallest estimated Area First (SAF). Using
simulations, we performed a thorough analysis of the cumulative
results for up to 180 weeks and considered three scheduling
objectives: waiting time, slowdown and per-processor slowdown.
We also evaluated other effects, such as the relationship between
job size and slowdown, the distribution of slowdown values,
and the number of backfilled jobs, for each HPC platform and
scheduling policy.
We conclude that one can only gain by replacing EASY-
backfilling with SAF with backfilling, as it offers improvements
in performance by up to 80% in the slowdown metric while main-
taining the simplicity and the transparency of FCFS. Moreover,
SAF reduces the number of jobs with large slowdowns and the
inclusion of a simple thresholding mechanism guarantees that no
starvation occurs. Finally, we propose SAF as a new benchmark
for future scheduling studies.

Supercomputers are producing large amount of data that need to be analyzed. They produce mostly two kinds of data: scientific data and monitoring data. Scientific data are the results of the execution of numerical simulations and need to be analyzed to extract knowledge. Monitoring data are produced by all kinds of sensors and software components, and can be analyzed to detect, among other things, reliability and performance issues. Considering the scale of such systems, the amount of data to process is huge and analyzing these data with short response time is often necessary. Using techniques and algorithms coming from the Big Data community seems appealing in this context.

This talk will present some of our efforts in trying to apply Big Data and Machine Learning techniques in the HPC context. It will cover 3 main topics: i) The use of Apache Spark Streaming as a tool for in-situ data analysis; ii) The analysis of time series to predict CPU overheating issues in Supercomputers; iii) The application of classification algorithms to the placement problem in NUMA platforms.