Cloud computing is traditionally defined in terms of data and compute services that support on-demand applications that scale to thousands of simultaneous users. High Performance Computing (HPC) is associated with massive supercomputers that run highly parallel programs for small groups of users. However, over the last five years, the demands of the scientific and engineering research community have created an evolutionary pressure to merge the best innovations of these two models. HPC centers have started to use cloud-native technologies like data object stores and cloud tools and processes to develop and deploy software. On the other side, cloud data centers are integrating advanced accelerators on each node and deploy high-performance interconnects with latency optimizations known from HPC. Furthermore, the AI revolution that was initially nurtured by the public cloud companies with their hyperscale datacenters, is increasingly finding adoption in the scientific and engineering applications on supercomputers.
On September 7th, 8th and 9th, as part of IEEE CLOUD 2021 (computer.org) we held the IEEE Services 2021 – IEEE International Symposium on Cloud HPC (CloudHPC) (computer.org). The program consisted of a plenary panel and 8 sessions with three speakers in each. Each session was recorded and over the next few weeks we will provide a review of the sessions and links to the videos.
In this initial sample we present the pre-recorded talks from Session 2. (We do this because the recording of the session was corrupted.)
Computational Biology at the Exascale.
Katherine Yelick, UC Berkeley and Lawrence Berkeley National Laboratory
In this talk Professor Yelick provides an excellent overview of the key algorithms of genomic and metagenomic data analysis and its importance for our society and environment. She also describes the challenges of adapting these algorithms to massive, distributed memory, exascale supercomputers. The presentation video is here.
HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework.
Judy Fox, University of Virginia
Professor Fox tackles the problem of doing genomic analysis workflows while preservice the privacy of individuals. Her team’s approach is to use uses new hardware supporting trusted execution environments which allows sensitive data to be kept isolated from the rest of the workflow. The presentation video is here.
An automated self-service multi-cloud HPC platform applied to the simulation of cardiac valve disease with machine learning.
Wolfgang Gentzsch, UberCloud, Founder & President
Dr. Gentzsch describes a large collaboration working on the Living Heart Project that provides a real-time cloud-based simulator for surgeons for repairing cardiac valve leakage. They use machine learning to reduce the problem for a 12-hour simulation to a 2 to 3-second prediction. The presentation video is here.
The full program of the symposium is below.
Panel Discussion
EXPLORING THE GROWING SYNERGY BETWEEN CLOUD AND HIGH-PERFORMANCE COMPUTING
Panelists:
Katherine Yelick, UC Berkeley and Lawrence Berkeley National Laboratory
Ian Foster, Argonne National Laboratory, University of Chicago
Geoffrey Fox, University of Virginia
Kate Keahey, Argonne National Laboratory, University of Chicago
Session 1 Cloud & Heterogeneous Architectures & Opportunities for HPC (Chair: Ian Foster)
Advancing Hybrid Cloud HPC Workflows Across State of the Art Heterogeneous Infrastructures
Steve Hebert, Nimbix Founder and CEO
The impact of the rise in cloud-based HPC
Brent Gorda, ARM Director HPC Business
HPC in a box: accelerating research with Google Cloud
Alexander Titus, Google Cloud
Session 2. HPCI in Biology & Medicine in the Cloud (Chair: Dennis Gannon)
Computational Biology at the Exascale
Katherine Yelick, UC Berkeley and Lawrence Berkeley National Laboratory
HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework
Judy Fox, Professor, University of Virginia
An automated self-service multi-cloud HPC platform applied to the simulation of cardiac valve disease with machine learning
Wolfgang Gentzsch, UberCloud, Founder & President
Session 3. Using HPC to Enable AI at Scale (Chair: Dennis Gannon)
Grand Challenges for Humanity: Cloud Scale Impact and Opportunities
Debra Goldfarb, Amazon, Director HPC Products & Strategy
Enabling AI at scale on Azure
Prabhat Ram, Microsoft, Azure HPC
Benchmarking for AI for Science in the Cloud: Challenges and Opportunities
Jeyan Thiyagalingam, STFC, UK, Head of SciML Group
Session 4. Applications of Cloud Native Technology to HPC in the Cloud (Chair: Christoph Hagleitner)
Serverless Supercomputing: High Performance Function as a Service
Kyle Chard, Professor, University of Chicago
Minding the Gap: Navigating the transition from traditional HPC to cloud native development
Bruce D’Amora, IBM Research
Composable Systems: An Early Application Experience
Ilkay Altintas, SDSC, Chief Data Science Officer
Session 5. Cloud HPC 1 session on HPC (Chair: Christoph Hagleitner)
T2FA: A Heuristic Algorithm for Deadline-constrained Workflow Scheduling in Cloud with Multicore Resource
Zaixing Sun, Chonglin Gu, Honglin Zhang and Hejiao Huang
A Case for Function-as-a-Service with Disaggregated FPGAs
Burkhard Ringlein, Francois Abel, Dionysios Diamantopoulos, Beat Weiss, Christoph Hagleitner, Marc Reichenbach and Dietmar Fey
Session 6. Distributed Computing Issues for HPC in the Cloud (Chair Geoffrey Fox)
Challenges of Distributed Computing for Pandemic Spread Prediction based on Large Scale Human Interaction Data
Haiying Shen, Professor, University of Virginia
GreenDataFlow: Minimizing the Energy Footprint of Cloud/HPC Data Movement
Tevfik Kosar, Professor, University of Buffalo & NSF
IMPECCABLE: A Dream Pipeline for High-Throughput Virtual Screening, or a Pipe Dream?
Shantenu Jha, Professor, Rutgers University
Session 7. Cloud HPC Barriers & Opportunities (Chair: Bruce D’Amora)
The Future of OpenShift
Carlos Eduardo Arango Gutierrez, Red Hat, HPC OpenShift Manager
Scientific Computing On Low-cost Transient Cloud Servers
Prateek Sharma, Indiana University
HW-accelerated HPC in the cloud: Barriers and Opportunities
Christoph Hagleitner, IBM Research
Session 8. Cloud HPC 2 session on HPC (Chair: Andrew Lumsdaine)
Usage Trends Aware VM Placement in Academic Research Computing Clouds
Mohamed Elsakhawy and Michael Bauer
Neon: Low-Latency Streaming Pipelines for HPC
Pierre Matri and Robert Ross
In the next post we will provide more details about each of these talks and links to the videos for each session