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The objective of this one-day workshop is to investigate opportunities in accelerating data management
systems and analytics workloads (which include traditional OLTP, data
warehousing/OLAP, ETL,
Streaming/Real-time, Analytics
(including Machine Learning),
and HPC/Deep Learning) using processors (e.g., commodity and specialized Multi-core, GPUs,
FPGAs, and ASICs), storage systems (e.g., Storage-class Memories like SSDs and
Phase-change Memory), and
programming models like MapReduce, Spark, CUDA, OpenCL, and OpenACC.
The current data management scenario is characterized by the following trends: traditional OLTP and
OLAP/data warehousing systems are being used for increasing complex workloads (e.g., Petabyte of data,
complex queries under real-time constraints, etc.); applications are becoming far more distributed, often
consisting of different data processing components; non-traditional domains such as bio-informatics, social
networking, mobile computing, sensor applications, gaming are generating growing quantities of data of
different types; economical and energy constraints are leading to greater consolidation and virtualization
of resources; and analyzing vast quantities of complex data is becoming more important than traditional
transactional processing.
At the same time, there have been tremendous improvements in the CPU and memory technologies.
Newer processors are more capable in the CPU and memory capabilities and are optimized for multiple
application domains. Commodity systems are increasingly using multi-core processors with more than 6
cores per chip and enterprise-class systems are using processors with 8 cores per chip,
where each core can execute
upto 4
simultaneous threads. Specialized
multi-core processors such as the GPUs have brought the computational capabilities of supercomputers
to cheaper commodity machines. On the storage front, FLASH-based solid state devices (SSDs) are
becoming smaller in size, cheaper in price, and larger in capacity. Exotic technologies like Phase-change
memory are on the near-term horizon and can be game-changers in the way data is stored and processed.
In spite of the trends, currently there is limited usage of these technologies in data management domain.
Naive usage of multi-core processors or SSDs often leads to unbalanced system. It is therefore important to
evaluate applications in a holistic manner to ensure effective utilization of CPU and memory resources. This
workshop aims to understand impact of modern hardware technologies on accelerating core components
of data management workloads. Specifically, the workshop hopes to explore the interplay between overall
system design, core algorithms, query optimization strategies, programming approaches, performance modelling
and evaluation, etc., from the perspective of data management applications.
The suggested topics of
interest include, but are not restricted to:
- Hardware and System Issues in Domain-specific Accelerators
- New Programming Methodologies for Data Management Problems on Modern Hardware
- Query Processing for Hybrid Architectures
- Large-scale I/O-intensive (Big Data) Applications
- Parallelizing/Accelerating Machine Learning/Deep Learning Workloads
- Autonomic Tuning for Data Management Workloads on Hybrid Architectures
- Algorithms for Accelerating Multi-modal Multi-tiered Systems
- Energy Efficient Software-Hardware Co-design for Data Management Workloads
- Parallelizing non-traditional (e.g., graph mining) workloads
- Algorithms and Performance Models for modern Storage Sub-systems
- Exploitation of specialized ASICs
- Novel Applications of Low-Power Processors and FPGAs
- Exploitation of Transactional Memory for Database Workloads
- Exploitation of Active Technologies (e.g., Active Memory, Active
Storage, and Networking)
- New Benchmarking Methodologies for Accelerated Workloads
- Applications of HPC Techniques for Data Management Workloads
- Acceleration in the Cloud Environments
Quantum
Computing and IBM Q: An Introduction
Carlos
Cardonha, IBM Research, Brazil
Abstract:
In his keynote speech at the Physics of Computation Conference in 1981, Richard Feynman discussed the challenges involved in the simulation of physical systems; in particular, Feynman suggested that quantum-mechanical devices should be constructed in order to make such tasks tractable, an observation that lead to the creation of several areas in science which we now know as Quantum Computing. After decades of intensive research efforts, answers for some of the main engineering challenges have been found, and the construction of quantum computers capable of overperforming classical computers seems not only possible, but eventually achievable in the near future. In this talk, we present the main concepts of quantum computing, some of the main challenges in the area, potential application in the near and long-term, and give an overview on resources that are currently available for learning about and interacting with quantum computers.
Bio:
Carlos Cardonha is a Research Staff Member of the Natural Resources Optimization Group at IBM Research Brazil, with a Ph.D. in Mathematics (T.U. Berlin) and with a Bachelor's and a Master's degree in Computer Science (Universidade de São Paulo). His primary research interests are mathematical programming and theoretical computer science, with focus on the application of techniques in mixed integer linear programming, combinatorial optimization, and algorithms design to real-world and/or operations research problems.
A
Comprehensive Study of SIMD Techniques for Data Processing:
The Good, the Bad, and the Ugly (Slides)
Shasank
Chavan, Oracle
Abstract:
Modern CPUs introduced SIMD (Single Instruction, Multiple Data)
instructions in the mid 1990s to drastically speed up multimedia
applications such as gaming and audio/video processing. It wasn’t
until the early 2000s when SIMD instructions started being leveraged
in data management systems to vectorize compute intensive workloads
on columnar data. Since then there has been a plethora of techniques
and algorithms introduced in academia utilizing SIMD instructions –
everything from optimizing traditional SQL operators such as scans and
joins, to accelerating graph, spatial and text processing. In this
talk we’ll advance through a list of top SIMD techniques developed
over the years for data management systems, and discuss in detail
what’s worked, what hasn’t, and what industry really needs going
forward in this age of GPUs, FPGAs, and specialized ASIC data
accelerators.
Bio:
Shasank Chavan is the Vice President of the In-Memory Technologies group at Oracle. He leads an amazing team of brilliant engineers in the Database organization who develop customer-facing, performance-critical features for an In-Memory Columnar Store which, as Larry Ellison proclaimed, “processes data at ungodly speeds”. His team implements novel SIMD kernels and hardware acceleration technology for blazing fast columnar data processing, optimized data formats and compression technology for efficient in-memory storage, algorithms and techniques for fast in-memory join and aggregation processing, and optimized in-memory data access and storage solutions in general. His team is currently hyper-focused on leveraging emerging hardware technologies to build the next-generation data storage engine that powers the cloud. Shasank earned his BS/MS in Computer Science at the University of California, San Diego. He has accumulated 15+ patents over a span of 20 years working on systems software technology.
Concepts
of Coherent Memory Interface
Sumanta Chatterjee, Oracle
Abstract:
Use of RDMA has benefited Enterprise Software with low latency, high
throughput I/O services. However, RDMA primitives pose many challenges
for developing distributed protocols. In this talk I will present
Coherent Memory Interface— modeled like Partitioned Global Address
Space (PGAS) to show how this new memory model can be used for
developing distributed protocols.
Bio:
Sumanta Chatterjee is Vice President of the Oracle Database Virtual OS
group. He works in the areas of Distributed Computing, I/O,
Concurrency Control, Memory Management areas. Sumanta joined Oracle
in 1994. Sumanta has a BTech from IIT Kanpur and a MS from the Texas
A&M University.
Workshop Co-Chairs
For questions regarding the
workshop please send email to contact@adms-conf.org.
Program Committee
- Raja Appuswamy, EPFL
- Shasank Chavan, Oracle
- Christoph Dubach, University of Edinburgh
- Markus Dreseler, HPI
- Stefan Manegold, CWI
- Bingsheng He, NUS
- Diego Arroyuelo, Universidad Técnica Federico Santa María
- Nikolay Sakharnykh, Nvidia
- Carsten Binnig, TU Darmstadt
- Kajan Kanagaratnam, IBM Analytics
- Bill Howe, University of Washington
- Wellington Martins, INF/UFG
- Arun Raghavan, Oracle Labs
- Ken Salem, University of Waterloo
- Rajkumar Sen, Striim Inc.
- Man Lung Yiu, Hongkong Polytechnic University
- Paper Submission: Monday, 11 June, 2018 (EXTENDED)
- Notification of Acceptance: Friday, 29 June, 2018
- Camera-ready Submission: Friday, 20 July, 2018
- Workshop Date: Monday, 27 August, 2018
Submission Site
All submissions will be handled electronically via EasyChair.
Formatting Guidelines
We will use the same document templates as the VLDB18 conference. You can find them here.
It is the authors' responsibility to ensure that
their submissions adhere
strictly to the VLDB format detailed here. In particular, it is not allowed to modify the format with the objective of squeezing in more material. Submissions that do not comply with the formatting detailed here will be rejected without review.
As per the VLDB submission guidelines, the paper length for a full paper is
limited to 12 pages, excluding
bibliography. However, shorter
papers (at least 4 pages of content) are encouraged as
well.
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