Tomer Morad, EE PhD Postdoctoral Fellow, Cornell Tech

Founder of DatArcs

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•  Traditionally operators manually sweep through parameters and search for best configuration for a benchmark

Today

• Labor intensive • Exploration of only a small number of knobs per workload • Needs to be done on a regular basis • Workload requirements varies between phases

Challenges

•  Dynamic Tuning!

Solution

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100’s of different knobs to tune!

Hardware

• SMT • Cache

partitioning • Hardware

prefetching • Peripheral

power states • …

Firmware

• Power Management Unit (PMU), DVFS, Power States

• CPU Microcode • …

Operating System

• Task Scheduler •  IO Scheduler • Page Cache • File

Prefetching Algorithm

• Memory Allocation Algorithm

• Affinity • …

Application

• Apache web server

• Choice of compiler

• Choice of libraries

• …

§  Source: Zhao, Zhengji, Nicholas J. Wright, and Katie Antypas. "Effects of Hyper-Threading on the NERSC workload on Edison." Proc. Cray User Group (2013).

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Hyperthreading improves performance with NAMD

Hyperthreading degrades performance with VASP

§  Source: http://www.phoronix.com/scan.php?page=article&item=linux_iosched_2012&num=3

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CFQ is better for “Initial Create” Deadline is better for “Read Compiled Tree”

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Run threads only when the increase in throughput justifies the extra power

Sample performance counters

Candidate ←Next task by CFS

Schedule Candidate

EPInew<EPIcur

Schedule Idle thread

Context Switch / Scheduling Interrupt

No

Yes

Yes

More Candidates?

No

Source: ‎Tomer Y. Morad, Noam Shalev, Idit Keidar, Avinoam Kolodny, Uri C. Weiser. “Energy-Efficient Task Assignment.” Submitted to the Journal of Parallel and Distributed Computing.

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3.2.0

Energy-Friendly Scheduler

Quad-core Intel i5-2500

8

-20%

0%

20%

40%

60%

Ener

gy S

avin

gs a

nd S

peed

up

System Energy Savings

Chip Energy Savings

Speedup

Affected Benchmarks

Knows the knobs in my

system

Tunes fast and

automatically

Adapts Optimizes for my metric

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Workload

Dynamic Tuner

Workload Classifier

Metrics Estimator

Metrics

Configuration Files

Knob Applier

Bucket

Estimations

Knob values

• Workload = currently running programs and their characteristics

• Bucket = a set of workloads that benefit from the same knob configuration

Objective: Classify

workload into buckets

• Number of useful buckets can’t be too low or too high

• Workload exhibits different behavior in each phase

Challenges

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• Metrics = Energy, performance, etc. Objective:

Gather System Metrics

• Platform energy estimation • Performance estimation • Each server generation has different

counters

Challenges

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Dynamic Tuner

•  Tune server for current workload Objective:

•  Knobs are not independent • Workload changes during the sampling period•  Environmental factors (temperature) impact the

metrics •  Balance between parameter exploration and

running the application normally

Challenges:

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§ DatArcs Optimizer is in closed beta § We’re looking for partners from the HPC community § We wish to learn about the needs of the HPC community

§ Please sign up for the beta: www.datarcs.com/downloads

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