The Energy Challenges of Caching and Moving Data On Your Chip

Speaker: Arrvindh Shriraman , Simon Fraser University

Date: Wednesday, April 08, 2015

Time: 4:00 PM to 5:00 PM Note: all times are in the Eastern Time Zone

Refreshments: 3:45 PM

Public: Yes

Location: 32-G531

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Host: Professor Daniel Sanchez, CSG - CSAIL - MIT

Contact: Sally O. Lee, 253-6837,

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Reminder Subject: TALK: The Energy Challenges of Caching and Moving Data On Your Chip

Today, power constraints determine our ability to keep compute units
active and busy. Interestingly, storing and moving the data used and
produced by the computation consumes more energy than the computation
itself. Whether multicores, GPUs or fixed-function accelerators, how we
move and feed the computation units has critical impact on the
programming model and the compute efficiency. We observe that unlike the
latency overhead of the data movement which could potentially be hidden,
energy overhead dictates that we need to fundamentally reduce waste in
the memory hierarchy.

Our research focuses on cache designs and coherence protocols that
improve the energy efficiency of the memory hierarchy by adapting the
data storage and movement to the application characteristics. I will
particularly focus on the design of a new coherence substrate, Temporal
Coherence, that helps build energy efficient cache hierarchies for both
GPUs and fixed-function accelerators. I will demonstrate how to realize
release consistency on a GPU system at low overhead and discuss the
improvements to the GPU programming model. I will also demonstrate how
temporal coherence can help offload fine-grain program regions to
fixed-function hardware accelerators and help move data efficiently
between the accelerators. The overall lessons from our work will
highlight the importance of optimizing the memory hierarchy with a focus
on energy efficiency.


Arrvindh is an Assistant Professor in the School of Computing Sciences
at Simon Fraser University where he has been a faculty member since
2011. His current research focuses on the energy efficient caches and
coherence protocols for fixed-function accelerators and GPUs. His recent
paper on GPU coherence has been selected as "Top Picks" by IEEE Micro
Magazine. He received an IBM Faculty Award in 2014 for his work in cache
coherence for accelerators.

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Created by Sally O. Lee Email at Thursday, February 26, 2015 at 3:52 PM.