[Colloquium] Reminder: Fang/Dissertation Defense/Apr 16, 2019

[Margaret Jaffey]

This is a reminder about Yuanwei (Kevin) Fang's dissertation defense
tomorrow. Also, please note the location change; it will be held in
JCL 298.

       Department of Computer Science/The University of Chicago

                     *** Dissertation Defense ***


Candidate:  Yuanwei Fang

Date:  Tuesday, April 16, 2019

Time:  11:00 AM

Place:  John Crerar Library (JCL) 298

Title: Extreme Acceleration and Seamless Integration of Raw Data
Processing

Abstract:
New sources of big data such as the Internet, mobile applications,
data-driven science and large-scale sensors (IoT) are driving demand
for growing computing performance. Efficient analysis of data in
native raw formats in real-time is increasingly important because of
rapid data generation, demand for analytics, and insights for
immediate responses. Traditional data processing systems can deliver
high-performance on loaded data, but transforming raw data into these
loaded formats is expensive. Data transformations, rather than
arithmetic operations, dominate the task performance. Such
transformation is a critical performance bottleneck of raw data
processing. We propose the ACCelerated Operators for Raw Data Analysis
(ACCORDA), a combined software and hardware approach, to accelerate
data analytics on unloaded raw data. ACCORDA enables real-time
decision making and fast knowledge exploration on dirty, diverse, and
ad-hoc raw data, such as fresh sensor data, web crawled, etc.

The Unified Transformation Accelerator (UTA) is ACCORDA’s hardware
approach. It creates flexible architecture support for data
transformation in analytical workloads. Exploiting efficient hardware
customization, a scratchpad memory, and MIMD parallelism, Unstructured
Data Processor (UDP) is a novel hardware accelerator based on the UTA
approach. UDP demonstrates the feasibility of the UTA approach. We
propose the UDP’s instruction set, micro-architecture, and compiler
toolchain. UDP has four unique features: multi-way dispatch,
variable-size symbol, flexible-source dispatch, and flexible
addressing. Extensive evaluation of data transformation kernels,
ranging from compression to pattern matching, shows UDP achieves 20x
average speedup and 1,900x energy efficiency when compared to an
8-thread CPU. The UDP’s implementation is >100x less power and area
than a single CPU core.

The Accelerated Transformation Operators (ATO) is ACCORDA’s software
approach. ATO integrates data transformation acceleration seamlessly,
enabling a new class of encoding optimization and robust,
high-performance raw data processing. The key enabler is UTA’s in
memory-hierarchy acceleration integration for efficient, low-overhead
data sharing with CPUs to unlock flexible software exploitation of
acceleration and worker thread integration. Furthermore, ATO extends
operator interface types with encoding, enabling new accelerated
operators to be included in query optimization. Runtime data formats
can be transformed to meet the encoding requirements of accelerated
operator implementations, and can be fused to further improve data
locality and save transformation cost. Together, the accelerator
integration and the ATO approach preserves existing system software
architectures and provides a uniform runtime to the execution engine,
empowering rule-based optimizers to drive flexible data-encoding based
optimization.

ACCORDA achieves significant acceleration on data transformation
tasks, with speedups up to 4.9x on regex matching, 2.6x on
decompression, 2x on parsing, and 20x on deserialization when compared
to an 8-thread CPU. We evaluate ACCORDA using end-to-end TPC-H queries
on unloaded data with raw format. Hardware acceleration contributes
1.1x-6.3x improvement alone, and software elements such as query
optimization for data encoding unlocked by ATO deliver an additional
1.2x-11.8x speedup. Combining UTA’s acceleration and ATO’s encoding
optimization, ACCORDA achieves 3.3x-13.2x overall speedups on single-
thread performance when compared to the baseline Spark SQL. We further
show that this performance benefit is robust across format complexity
of query predicates and selectivity (data statistics). Furthermore,
ACCORDA robustly matches or even outperforms (by up to 11.4x) prior
systems that depend on caching transformed data, while computing on
raw, unloaded data.

Yuanwei's advisor is Prof. Andrew Chien

Login to the Computer Science Department website for details,
including a draft copy of the dissertation:

 https://newtraell.cs.uchicago.edu/phd/phd_announcements#fywkevin

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Margaret P. Jaffey            margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (Ry 156)               (773) 702-6011
The University of Chicago      http://www.cs.uchicago.edu
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