1. Dagstuhl Seminar on Dark Silicon: From Embedded to HPC Feb 3, 2016
HPC Group
Dagstuhl Seminar on Dark Silicon: From Embedded to HPC
Feb 3, 2016

2. What is Dark Silicon in HPC?
Shortage of power, strict constraints
Overprovisioning of hardware resources with respect to power
Chip-level: we already have dark silicon
Job-level, cluster-level: workload dependent, dim silicon

3. Main challenges arising from DS?
Focus in HPC is performance, we care about FLOPS, time to solution, accuracy of science. Different than the embedded community
Stay in a power band, fluctuations of a few megawatts may arise, power stability
Performance variability, reproducibility
System throughput and power utilization: how do we manage resources?

4. Challenges…
Performance Variability:
Embrace it and build runtime systems to address this
At the cost of performance?
Use it to our advantage: need more control?
Minimize in hardware?
How do we tolerate variability?
Current approaches for programming the machines and performance tuning will no longer work.
We need to redefine the metrics on how we measure things. We assume homogeneity at the moment.

5. Fundamental Techniques
Monitoring at large scale (TUM, LRZ, LLNL, ETH, Dresden, JSC …)
Granularity, what to gather, how to use it
Resource Management, RMAP/Flux (LLNL, TUM, ETH, UniBo…)
Runtime Systems
GEO PM (Intel), Conductor (LLNL), SDSC, PowSched (UOregon)
Tracing, Visualization (LLNL, JSC, Dresden, Oregon,SDSC, …)
Deeper connection with workflows and applications. Also, refactoring of applications (work stealing, oversubscription, auto tuning)

6. Opportunities and Impacting the Embedded community?
Measurement and Analysis Tools
Learning from the operating systems community about hardware management
Real-time boundaries, can we introduce them in HPC, have a QOS definition?
Programming models tailored to code (right techniques for programming)
Portability, development cycle
Hardware-software co-design
Redesign the stack: hardware, job, cluster, site

7. Going Forward: Big Data and HPC
HPC Applications are handling more data, which creates a new set of challenges…
Data-intensive computing, data-aware computing
Another Dagstuhl? 