David Black-Schaffer
Professor at Department of Information Technology, Division of Computer Systems
- Email:
- david.black-schaffer[AT-sign]it.uu.se
- Telephone:
-
+4618-471 6830
- Mobile phone:
-
+46 76 8242017
- Visiting address:
- Room
POL 105165 hus 10, Lägerhyddsvägen 1
- Postal address:
- Box 337
751 05 UPPSALA
Short presentation
I investigate modeling and measuring the effects of shared memory resources (caches and off-chip bandwidth) in multicore processors on power and performance. My work addresses both theoretical models and techniques for measuring actual behavior on real systems. Currently I am applying these techniques to improve task scheduling on heterogeneous systems, to predict power and performance, and to develop smart memory systems.
Keywords: computer architecture memory systems simulation runtimes scheduling
I received my PhD in Electrical Engineering from Stanford University
in 2008. My PhD thesis was on programming for real-time embedded processing on many-core processors
in the Concurrent VLSI Architecture Group
working with William Dally
. After my PhD I worked at Apple on the development of the first OpenCL
implementation for heterogeneous parallel processing across CPUs and GPUs, and then as a postdoc researcher in computer architecture in the Dept. of Information Technology at Uppsala University
. I was appointed assistant professor in 2010 in the architecture research group at Uppsala looking at parallel programming systems and optimizations as part of the UPMARC research project. In 2014 I was promoted to associate professor (docent, lektor).
In addition to research, I lead the ScalableLearning project to bring the benefits of active, flipped-classroom teaching
to thousands of students in Sweden and abroad.
Grants and Awards
- European Research Council ERC Starting Grant
(2017-2022)
-
Uppsala University Pedaogical Prize
(2016)
- Swedish Foundation for Strategic Research (SSF), Smart Systems Framework Grant
(Co-PI, 2016-2021) Automating System SpEcific Model-Based LEarning (ASSEMBLE)
- Knut and Alice Wallenberg Foundation, Wallenberg Academy Fellow
(2016-2021)
- Swedish Research Council (VR), Young Researcher Project Grant (2015-2018)
- Swedish Foundation for Strategic Research (SSF), Future Research Leaders
(2013-2018)
- EU FP7, Addressing Energy in Parallel Technologies
(Co-PI 2013-2016)
- Uppsala University, Pedagogical Development Grant for Flipped Classroom (2013)
- Swedish Research Council (VR), Framework Grant (Co-PI, 2012-2017)
- Uppsala Union of Engineering and Science Students, Teaching Award
(2012)
- Stanford University, Centennial Teaching Assistant Award
(2004)
- Stanford University, Hugh Hildreth Skilling Teaching Assistant Award (2003)
Teaching
-
Computer Architecture 1 (To view the interactive online course lectures, register at ScalableLearning
and join with the enrollment key YRLRX-25436.)
- Sample: Introduction to Digital Logic Design
(88 minutes)
- Sample: Introduction to Virtual Memory
(70 min)
- Sample: Introduction to Digital Logic Design
-
Parallel Programming for Efficiency (MSc level)
- Sample: Power and Energy in Computer Systems
(52 min)
- Sample: Power and Energy in Computer Systems
- Introduction to Computer Architecture Research (PhD level)
Presentations
-
Predicting Next-Generation Multicore Performance in a Fraction of a Second
(Keynote, SICS Multicore Day, 2015)
-
GPUs: The Hype, The Reality, and the Future
(Keynote, SICS Multicore Day, 2013) PDF
(2011)
-
Flipped Classroom Teaching in an Introductory CS Course
(KTH, 2013) PDF
-
Resource Sharing in Multicore Processors
(Keynote, Ericsson Software Research Day 2011)
-
Introduction to OpenCL
PDF
-
Optimizing OpenCL
PDF
- GPU Architectures for Non-Graphics People PDF
This paragraph is not available in English, therefore the Swedish version is shown.
My research focuses on improving efficiency in computers by making the memory system more intelligent. Our work includes more clever ways of moving and placing data in the memory system, integrating data movement with the processor core itself, adapting runtime schedules for better data movement, and the analysis and modeling of data movement.
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