Welcome!
We perform research in the broadly defined area of
computer engineering. We design new hardware
architectures for emerging platforms, spanning
datacenters to mobile devices. Moreover, we
propose new hardware management strategies to
ensure service quality for diverse users in
complex systems. In design and management, we
navigate fundamental relationships between
performance, energy-efficiency, and fairness.
1 Apr 20 | IEEE Spectrum publishes our perspective on game theory for datacenter management. A great article for a high-level introduction. |
30 Mar 20 | Pengfei Zheng defends his PhD dissertation on machine learning for datacenter operations. Congratulations! |
23 Mar 20 | Calvin Ma presents his thesis, on time series analysis for straggler prediction, for graduation with distinction in computer science. Well done! |
8 Mar 20 | Our SOSP 2009 paper -- Better I/O through byte-addressable persistent memory -- receives the Persistent Impact Prize for its exceptional impact on non-volatile memory research. |
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Computer Systems and Machine Learning
We adapt and invent methods in statistical machine
learning to understand and optimize distributed
systems. We focus on interpretable frameworks such
as causal inference and natural-language
processing. We also focus on dynamic frameworks
such as reinforcement learning.
- Causal inference
[SIGMETRICS'18]
[MICRO'12]
- Dynamic mechanisms
[MICRO'19]
[SIGMETRICS'18]
Computer Systems and Economics
With the democratization of cloud computing,
diverse users demand computation from complex
datacenters. In this setting, we study mechanisms
for hardware allocation and scheduling. Our
interdisciplinary research spans computer
architecture, economic mechanism design, and game
theory. First, we examine welfare maximization and
markets in which autonomic agents bid for hardware
on behalf of users. Second, we investigate
fairness and algorithms that equitably divide
hardware among strategic agents. Finally, we
navigate tensions between welfare and fairness.
- Game theory
[IEEE'20]
[HPCA'17]
[ASPLOS'16]
[ASPLOS'14]
- Market mechanisms
[HPCA'18]
[HPCA'13]
Datacenter Design for Efficiency
To keep pace with big data computing, datacenters
must provide more capability within today’s power
budgets. Toward this goal, we architect servers
using hardware originally intended for mobile
systems. For some datacenter applications,
mobile-class processors and memories are suitable
and far more energy-efficient than their
server-class counterparts. For others, heterogeneous
datacenters, with a mix of server- and mobile-class
hardware, mitigate latency penalties and ensure
service quality.
- Risk and heterogeneous system design
[HPCA'14]
- Mobile-class DRAM for datacenters
[MICRO'12]
[ISCA'12]
- Mobile-class processors for datacenters
[ISCA'10]
Specialized and Adaptive Architecture
With the end of Dennard scaling, Moore’s Law
provides more transistors but increases power
densities. Moreover, Amdahl’s Law says that a
multi-core strategy alone is insufficient. We
study hardware specialization and its benefits for
energy efficiency. Specialization tailors
resources to application requirements whether
through heterogeneous processors, adaptive
microarchitectures, or application-specific
accelerators. We study automated design space
exploration to reduce the non-recurring
engineering costs of specialization. Moreover, we
study policies and mechanisms for managing
adaptive microarchitectures.
- Sources of inefficiency in general-purpose design
[ISCA'10]
- Efficiency from adaptive microarchitectures
[ASPLOS'08]
- Efficiency from heterogeneous microarchitectures
[HPCA'07]
Scalable Technology
We coordinate architecture and circuit design and
identify new system capabilities enabled by
emerging technologies. We study phase change
memory (PCM), which relies on programmable
resistance to provide qualitatively better scaling
trajectories than today’s DRAM. We architect PCM
on the memory bus to expose its fast
non-volatility. Our architectures increase
capacity and reduce power yet offer performance
that is competitive with existing DRAM-based
systems. Our research spans the hardware-software
interface, from links to file systems.
- Coordinated architecture and circuit design
[ISCA'10]
- Phase change memory architectures
[ISCA'09]
[HPCA'13]
- Phase change memory and file systems
[SOSP'09]
Design Methodology
We apply statistical inference to capture broad
relationships within parameter spaces for
microarchitectures and multiprocessors, providing
new answers to previously intractable
questions. Inference defines a design space,
simulates sparsely sampled designs, and derives
predictive models to act as surrogates for more
expensive architectural simulators. Moreover,
inference is applicable across the
hardware-software interface, whether estimating
the impact of process variations or estimating the
performance of parallel applications.
- Rethinking digital design
[Micro'10]
- Inferring models for multiprocessors
[MICRO'08]
- Inferring models for processor cores
[ASPLOS'06]
[HPCA'07]
High-Performance Software
Performance is often correlated with energy
efficiency. For example, locality-enhancing
optimizations not only reduce execution time but
also reduce communication energy. We characterize
the determinants of performance for datacenter and
supercomputing software. The characterization
drives our research in performance
optimization. We study software run-times that
mitigate communication costs. And we study
heuristics that automatically tune algorithms,
data structures, and code to reflect evolving
compiler and hardware technologies.
- Query complexity in web search
[ISCA'10]
[ISLPED'13]
- Modeling and tuning parallel applications
[PPoPP'07]
- Modeling and tuning sparse linear algebra
[SC'02]
[ICPP'04]
Technology Policy
The increasing centralization of compute resources
suggests environmental effects from IT
infrastructure are most effectively monitored in
an optimized for large-scale datacenters. Within
datacenters, new hardware architectures can
provide energy efficiency. Equally important is
validating claims of net environmental benefits
from adopting digital practices and forgoing
conventional ones. Understanding substitution
effects will be important. Our current research in
digital sustainability links fundamental
technology, business management, and public
policy.
- Corporate social responsibility
[StGallen'08]
- Digital sustainability
[StGallen'07]
