1. Toward a Unified HPC and Big Data Runtime
Joshua Suetterlein
University of Delaware
Joshua Landwehr, Joseph Manzano, Andres Marquez
PNNL
11/18/2018

2. Why should we consider joining HPC and Big Data?
Motivation
Why should we consider joining HPC and Big Data?
Boundaries are beginning to blur
Next generation systems can cost Billions of $$$ in R&D and Millions to maintain
Moreover technical challenges provide a unique opportunity
HPC
Big Data
Computational science has become essential for engineering and science
Reliably manages exponentially growing data
Computational models enable the impossible (or impractical)
Data analytics explores complex relationships among and growing data
11/18/2018

3. Motivation Cont. Known Exascale Challenges
Energy efficiency
Resilience
Memory technology
Interconnect technology
Algorithms
Scientific productivity
Hidden Challenge: I/O vs Concurrency
System
2009
2015
2024
Total Concurrency
225,000
O(million)
O(billion)
System Memory
0.3 PB
5 PB
10 PB
I/O
0.2 TB
10 TB/s global PFS +
100 TB/s burst buffer
20 TB/s global PFS +
500 TB/s burst buffer
Science at Extreme Scale: Architectural Challenges and Opportunities – Lucy Nowell April, 2014
11/18/2018

4. Motivation Cont. How will we perform science?
Increased parallelism enables larger experiments
Weak scaling – increase concurrency ≈ more data
Current paradigm – offload results to another machine i.e. post-mortem analysis
New paradigm – leverage In-situ/Big Data analytics then offload reduced results
Another view: If a simulation is the source, we can leverage streaming analytics to hide IO latency
Synergistic Challenges in Data-Intensive Science and Exascale Computing. March, 2013
11/18/2018

5. Fine-Grain How is computational science traditionally performed?
MPI + OpenMP dominate scientific code
Architectural trends previously amenable to OpenMP and MPI are changing
Unprecedented concurrency
Highly coupled memory and processing elements
Deepening memory hierarchies
Increasingly heterogeneity
Strict energy and power budgets
A dataflow inspired, asynchronous, event driven, fine-grain execution model appears flexible enough to express and exploit sufficient parallelism to utilize new architectures
Gao. 7/2002
Fran Allen’s Retirement Workshop
11/18/2018

6. with Big Data Extensions
Fine-Grain Cont.
Open Community Runtime (OCR) is a codelet execution model developed under UHPC and X-Stack initiatives
Exploits asynchronous, event driven, fine-grain execution
Can OCR be augmented with streaming Big Data techniques to reduce IO pressures and hide latency?
Application
Fine-grain runtime
with Big Data Extensions
RDMA
Scheduler (SLURM)
File System
(Lustre)
Compute Nodes
11/18/2018

7. Opportunities
Fine-grain runtimes have the potential of providing more than weak scaling
For Exascale, system memory is getting bigger (~10PB)
Big Data trends provide a new abstractions for analytics
In-situ has not been very flexible
Events, memory abstractions, and compression are already being explored
11/18/2018

8. Challenges Programming model disconnect Load balancing
How can we effectively map simulated data generated by fine-grain tasks into partitioned key/value
Load balancing
Transparent parallel execution of a partition of data
Priorities: Simulation vs Analysis
This is similar to streaming back-pressure except we control the source…
How does this effect the critical path?
11/18/2018

9. Questions
Thank You!
11/18/2018