Dr Jeremy Singer, University of Glasgow
25 February 2021

Abstract:
Love ’em or hate ’em, interactive computational notebooks are here to stay as a mainstream code development medium. In particular, the Jupyter system is widely used by the data science community. This presentation explores some use cases for programmatic introspection of a Jupyter notebook from within a notebook itself. We sketch a possible reflection API for Jupyter and describe how its implementation is complicated by the under-the-hood message flows of the Jupyter distributed system architecture.

Bio:
Jeremy Singer is a senior lecturer in the School of Computing Science at the University of Glasgow, where he has worked for the past 10 years. Jeremy’s research interests include programming language compilers and runtimes, memory management, manycore parallelism, and distributed systems. He currently co-leads the EPSRC-funded Capable VMs project. Jeremy is the author of the textbook “Operating System Foundations with Linux on the Raspberry Pi” and lead educator of the “Functional Programming in Haskell” massive open online course.

Dr Giorgis Georgakoudis, Lawrence Livermore National Laboratory
18 February 2021

Abstract:

This talk will present an overview of research on different areas of open problems in HPC. On fault tolerance, Giorgis will present the Reinit solution for fault tolerance in large scale MPI applications. Reinit improves the recovery time of checkpointed MPI applications by avoiding MPI re-deployment on restart, extending instead the MPI runtime to repair itself at runtime. On performance portability, Giorgis will present the auto-tuning framework Apollo, which provides an API for tuning execution parameters of code regions using machine learning at runtime. Lastly, Giorgis will talk on understanding deficiencies of parallelism compilation and of the approach to move forward for improving compiler optimizations for parallel programs.

Bio:

Giorgis Georgakoudis is a Computer Scientist in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research interests include fault tolerance, optimized compilation of parallel programs, and runtime performance characterization and tuning. He is currently involved in the Exascale Computing Project, designing and developing fault-tolerance abstractions for MPI, and in the Apollo project for dynamic tuning the performance of parallel programs. Also, since October 2020, Giorgis leads his own project on compiler optimizations for parallelism as the Principal Investigator, funded through the Lab Directed Research and Development (LDRD) Program of LLNL.

Giorgis obtained his Dipl. Eng. (2007), Master’s (2010), and PhD degrees (2017) from the Department of Electrical and Computer Eng. of University of Thessaly, Greece. From 2013 to 2018, he was also affiliated with Queen’s University Belfast, UK working as a researcher concurrently with his PhD studies. Since November 2018 Giorgis is working in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. He is also a member of ACM and IEEE societies, and frequently provides professional service as a reviewer in conferences and journals.

Dr Julian Shun, Massachusetts Institute of Technology
14 January 2021

Abstract: This talk presents new parallel algorithms for density-based
spatial clustering (DBSCAN) on point sets and structural clustering
(SCAN) on graphs, two problems that have received significant
attention due to their applicability in a variety of data analysis
tasks.

Existing parallel algorithms for DBSCAN require much more work than
their sequential counterparts, making them inefficient for large
datasets.  We bridge the gap between theory and practice of parallel
DBSCAN by presenting new parallel algorithms for Euclidean exact
DBSCAN and approximate DBSCAN that match the work bounds of their
sequential counterparts, and are highly parallel (polylogarithmic
depth).  For graphs, we present the first parallel index-based SCAN
algorithm, based a recent sequential algorithm, which enables users to
efficiently explore many different parameter settings for cluster
generation. Our parallel algorithm has a better work bound than the
sequential algorithm, and achieves logarithmic depth. We also apply
locality-sensitive hashing to design a novel approximate SCAN
algorithm and prove guarantees for its clustering quality.  We present
optimized implementations of our algorithms which achieve good
parallel scalability and outperform existing parallel implementations
by up to several orders of magnitude.

Bio: Julian Shun is the Douglas T. Ross Career Development Assistant
Professor of Software Technology in EECS and CSAIL at MIT.  Prior to
coming to MIT, he was a Miller Research Fellow at UC Berkeley.  He
received his Ph.D. from Carnegie Mellon University and his B.A. from
UC Berkeley.  His research focuses on the theory and practice of
parallel algorithms and programming frameworks.  He has received the
NSF CAREER Award, DOE Early Career Award, ACM Doctoral Dissertation
Award, CMU School of Computer Science Doctoral Dissertation Award,
Facebook Graduate Fellowship, Google Faculty Research Award, and best 
paper awards at PLDI, SPAA, and DCC.