Callisto

The Callisto project investigates how systems software can evolve to better support parallel and distributed runtime systems on tightly-coupled clusters and on large NUMA systems.

Project Details

Callisto

Callisto

The Callisto project investigates how systems software can evolve to better support parallel and distributed runtime systems on tightly-coupled clusters and on large NUMA systems.

Project Overview

Modern h/w and s/w looks radically different to the HPC and server workloads which have traditionally motivated parallel programming: Individual machines are internally more complex with multiple processor types, NUMA domains and hot-swap components. Workload resource demands are also more complex, with bursty behavior over short timescales, and an ability to elastically use more/fewer resources as they are available. Furthermore, the need to consolidate workloads on large machines or clusters brings a renewed need to handle resource management on behalf of multiple jobs and multiple users.

In Callisto, we are looking at how to re-architect the interaction between virtualization, distribution, and parallel runtime systems in order to better support these new workloads.

A particular focus is the needs of parallel and distributed graph analytics workloads (such as those generated by the Green-Marl DSL) when deployed on large NUMA systems, or on clusters of machines connected by a low-latency interconnect.

Principal Investigator

Sungpack Hong

Senior Research Director

Sungpack is a Research Director at Oracle Labs. He joined Oracle at 2012 after getting his PhD from Stanford University. He leads several research projects regarding graph data processing, large scale data analytics, domain-specific language and machine learning.

Education:

* PhD EE, 2013, Stanford University

* MS EE&CS, 2001, KAIST, South Korea

* BS Computer Science, 1999, KAIST, South Korea

Publications