Projects

My research work is primarily in High Performance Computing at different levels of the software stack. It includes performance optimization, resource management, and fault tolerance techniques for scientific applications as well as for stochastic workflows that do not traditionally fit the HPC model.

Speculative Scheduling

Reservation-based batch scheduling using priority queues and backfilling algorithms is the current de facto solution in implementing HPC schedulers. These systems are designed for traditional scientific applications and can have suboptimal performance for new emerging classes of applications. These applications develop modeling and simulation workflows with unpredictable resource requirements and focus primarily on productivity and not performance (like neuroscience and bioinformatics).

This project focuses on the differences between typical HPC scientific applications and stochastic workflows in order to design and implement new computational models for engaging resources at large scale in novel ways to accommodate their specific needs.

Simulator

For our experiments we built a simulator for HPC scheduelers, called ScheduleFlow.

The ScheduleFlow software consists of a series of scripts and classes that offer an API allowing users to create simulation scenarios for any type of online and reservation-based batch schedulers.

Software

The simulator has been extended to allow speculative scheduling by overwriting the amount of requested resources by an application to values based on the past behavior patterns. The code is open source and available on GitHub

You are encouraged to contribute to ScheduleFlow or SpeculativeScheduling. Questions and bugs can be reported through GitHub by creating a new issue in the corresponding repository with the “bug” or “question” tags.

I/O congestion

Many scientific HPC applications generate or deal with TeraBytes of data during their lifetime (for example, the Large Hadron Collider generates 15PB/year, light source projects deal with 300TB of data per day and climate modeling are expected to have to deal with more than 100EB of data). Moreover, I/O throughput and memory access time has an order of magnitude slower increase rate than FLOPs for the new generation of supercomputers.

To help with the ever growing amount of data created, architectural improvement such as burst buffers have been added to the system. In addition, work is being done to transform the data before sending it to the disks in the hope of reducing the I/O sent. However, observations show that I/O transfer can still be slowed down up to 70% due to congestion on current HPC systems.

This project investigates different optimization solutions to be included in the I/O middleware in order to alleviate the impact of congestion on applications.

Fault tolerance

HPC sysytems today contain more than 100,000 processing and memory units. With an individual MTBF (Mean Time Between Failures) for one unit of, one century, an HPC system will encounter a failure every 9 hours in average, which is smaller than the execution time of many HPC applications. In addition to fail-stop failures, silent errors are not detected immediately, but instead after some arbitrary detection latency can cause applications to degrade their performance, crash or reach a false result.

This project investigates performance variability issues and resiliency properties of scientific applications and HPC systems. We are working on understanding the intrinsic applcication resiliency to silent errors and how to leverage it to optimze the amount of resources required by a successful run (for example, by decreasing the precision of computations and reducing the energy and memory footpring of an application) The project also includes designing new fault tolerance methods by including preventive methods based on hardware counters and application memory, network and computational patterns to optimizing current checkpointing strategies.

System level tools

HELO (Hierarchical Event Log Organizer)

A tool for extracting event templates from large datasets and updating them as new events get generated. HELO presents an intuitive output to system administrators. It is currently integrated in the Blue Water software stack.