1. IEEE-HKN Chapter at Wichita State
Wichita, KS
“High-Performance Computing Systems and Our Future”
Understanding the world’s biggest mysteries (such as human genome and climate change) would be impossible without computers. Supercomputers operates at Peta (10^15) FLOPS; however, require tens of millions of dollars. High-performance computing (HPC) offers similar processing speed (10^12 FLOPS) with more affordability, accessibility, and ease-of-use. This presentation will introduce HPC systems and examples how HPC solves big data problems effectively, and discuss the societal benefits of research on HPC systems.
Presenter: Abu Asaduzzaman
Associate Professor of Computer Engineering,
Director of CAPPLab, and Advisor of the IEEE Student Branch
November 16, 2017

2. “High-Performance Computing Systems and Our Future”
Outline ►
Introduction
We and Our Future
High-Performance Computing (HPC) Activities
HPC Systems
Computer Systems
SMT-Capable CPU-GPU Systems
Examples: HPC Systems for Big Data
Matrix Multiplication, Graph
CAPPLab Research
Concluding Remarks

3. “High-Performance Computing Systems and Our Future”
We and Our Future
When/where Eta Kappa Nu (HKN) was founded first?
In 1904 at UIUC by Maurice L. Carr [1]
How about the origin of HKN?
Greek word for electron: ΗΛΕΚΤΡΟΝ [1]
What was the original goal of HKN?
… to help electrical engineering graduates find employment [2] …
Today what are the fields that HKN does recognize?
The IEEE-designated fields: Engineering, Computer Sciences and Information Technology, Physical Sciences, Biological and Medical Sciences, Mathematics, Technical Communications, Education, Management, and Law and Policy. [2]
[1] “About HKN,”
[2] “Qualifications for IEEE,” Membershiphttps://

4. “High-Performance Computing Systems and Our Future”
We and Our Future (+)
Industry
Apply and Research
Academia (and other organizations)
Teach and Research

5. “High-Performance Computing Systems and Our Future”
We and Our Future (+)
National Association of Colleges and Employers (NCAA) SALARY SURVEY 2017 [1]
[1]

6. “High-Performance Computing Systems and Our Future”
We and Our Future (+)
Michigan Tech – 2018 Engineering Salary Statistics– Engineers Get Top Pay [1]
Computer Engineering
Hardware
Software
Electrical Engineering
Mechanical Engineering
[1]

7. “High-Performance Computing Systems and Our Future”
High-Performance Computing (HPC) Activities
What is high-performance computing?
HPC generally refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. [1]
Multicore central processing unit (CPU) and many-core graphics processing unit (GPU) cards are typically used to build HPC systems.
[1]

8. “High-Performance Computing Systems and Our Future”
HPC in Academia
Google search
Google search

9. “High-Performance Computing Systems and Our Future”
HPC Research in NSF
Google search
Google search

10. “High-Performance Computing Systems and Our Future”
U.S. Leadership in HPC [1, 2]
[1]
[2]

11. “High-Performance Computing Systems and Our Future”
HPC Research at WSU

12. “High-Performance Computing Systems and Our Future”
Outline ►
Introduction
We and Our Future
High-Performance Computing (HPC) Activities
HPC Systems
Computer Systems
SMT-Capable CPU-GPU Systems
Examples: HPC Systems for Big Data
Matrix Multiplication, Graph
CAPPLab Research
Concluding Remarks

13. “High-Performance Computing Systems and Our Future”
Computer Systems
Google search
Google search

14. “High-Performance Computing Systems and Our Future”
Computer Systems
Google search
Google search

15. “High-Performance Computing Systems and Our Future”
SMT-Capable CPU-GPU Systems = HPC Systems
SMT – Simultaneous Multi-Threading
CPU – Central Processing Unit
GPU – Graphics Processing Unit
Pure Harvard Architecture Von Neumann Arch
A Computer Architecture
Name of the Game: performance, energy consumption, cost, …

16. “High-Performance Computing Systems and Our Future”
CPU-GPU Systems (+)
Time-Efficient Computing
SMT – Simultaneous Multi-Threading
CPU – Central Processing Unit
GPU – Graphics Processing Unit
Compute Unified Device Architecture (CUDA) – a parallel computing platform and application programming interface (API) model created by Nvidia

17. “High-Performance Computing Systems and Our Future”
SMT Capable CPU-GPU System
CPU-GPU Systems (+)
SMT – Simultaneous Multi-Threading
CPU – Central Processing Unit
GPU – Graphics Processing Unit
Many-Core GPU Card
Multicore CPU
Instruction Execution
A process is a running program. A process can
generate many processes (called threads). …

18. “High-Performance Computing Systems and Our Future”
SMT-Capable CPU-GPU Systems = HPC Systems
CL1 – Level-1 Cache
CL2 – Level-2 Cache
Cache and memory are very
power-hungry. More energy
consumption, more heat dissipation!
HPC: CPU i7-980X 130W,
GPU Tesla K80 300W [1, 2]
Tianhe-1A consumes 4.04 MW;
for 4 MW at $0.10/kWh
is $400 an hour or
about $3.5 million per year. [3]
CL1
CL2
Name of the Game: performance, energy consumption, cost, …
[1]
[2]
[3]

19. “High-Performance Computing Systems and Our Future”
SMT-Capable CPU-GPU Systems = HPC Systems
HPC Systems
If SMT-capable 16-core CPU and 5000-core GPU card are used to build a HPC system, it offers about 9 Tera (10^12) FLOPS and costs about $5K. [1]
HPC: CPU i7-980X 130W, GPU Tesla K80 300W
Supercomputers
A supercomputer may have more or less 300,000 processing cores and operate at Peta (10^15) FLOPS; however, it costs tens of millions of dollars. [2, 3]
Tianhe-1A: 4.04 MW (about $3.5 million per year)
Name of the Game: performance, energy consumption, cost, …
[1]
[2]
[3]

20. “High-Performance Computing Systems and Our Future”
Outline ►
Introduction
We and Our Future
High-Performance Computing (HPC) Activities
HPC Systems
Computer Systems
SMT-Capable CPU-GPU Systems
Examples: HPC Systems for Big Data
Matrix Multiplication, Graph
CAPPLab Research
Concluding Remarks

21. “High-Performance Computing Systems and Our Future”
HPC Systems for Big Data
Matrix Multiplication (MM)
[C] = [A] [B]
2 x 2 Matrix
8 (i.e., 2 * 2^2) multiplications
4 (i.e., 1 * 2^2) additions

22. “High-Performance Computing Systems and Our Future”
HPC Systems for Big Data
Matrix Multiplication (MM)
2 x 2 Matrix: (2^3) 8 multiplications
3 x 3 Matrix: (3^3) 27 multiplications
4 x 4 Matrix: (4^3) 64 multiplications
Are we reducing #*s? (no)
What is the message?
For many 2 x 2 matrix solvers (with 8 MULT),
it takes “only” 2 * 8 MULT time unit (6416?)
Do we have many solvers/cores? (Yes, GPU) A B C

23. “High-Performance Computing Systems and Our Future”
HPC Systems for Big Data
GPGPU/CUDA Technology
GPU (the chip itself) consists of group of Streaming Multiprocessors (SM)
Inside each SM:
32 cores (sharing the same instruction)
64KB shared memory (shared among the 32 cores)
32K 32bit registers
2 warp schedulers (to schedule instructions)
4 special function units

24. “High-Performance Computing Systems and Our Future”
HPC Systems for Big Data
Matrix Multiplication with CUDA for Graph [1]
A graph is a representation of a set of objects (i.e., vertices) where some pairs of objects are connected by links (i.e., edges).
To solve Engineering, Science, and, Math, problems
Is there any path between vertices C and J?
Yes. C-D-F-G-J (4). C-E-H-G-J (4). C-E-H-J (3).
Number of paths of length 3 between two vertices?
GPU Computing / Matrix Multiplication help solve graph problems effectively
[1] “Matrix Multiplication with CUDA --- A basic introduction to the CUDA programming model,” by Robert Hochberg

25. “High-Performance Computing Systems and Our Future”
HPC Systems for Big Data
Matrix Multiplication with CUDA for Graph [1] (+)
Number of paths of length 4 between C and J?
[1] “Matrix Multiplication with CUDA --- A basic introduction to the CUDA programming model,” by Robert Hochberg

26. “High-Performance Computing Systems and Our Future”
HPC Systems for Big Data
Matrix Multiplication with CUDA for Graph [1] (+)
Number of paths of length 4 between C and J?
[1] “Matrix Multiplication with CUDA --- A basic introduction to the CUDA programming model,” by Robert Hochberg

27. “High-Performance Computing Systems and Our Future”
HPC Systems for Big Data
Matrix Multiplication with CUDA for Graph [1] (+)
Number of paths of length 4 between C and J? 7
Matrix multiplication
Row (# of paths of length 1)
x Column (# of paths of length 3)
 Column (# of paths of length 4)
Length 3 
Length 4 
Length 1 
[1] “Matrix Multiplication with CUDA --- A basic introduction to the CUDA programming model,” by Robert Hochberg

28. “High-Performance Computing Systems and Our Future”
HPC Systems for Big Data
NVIDIA Tesla M2070 Performance
14 multiprocessors, 448 cores
For matrices 1000x1000, approach a ratio of 1.7
For matrices 8000x8000, the shared memory solution is faster by a factor of 2.6
[1] “Matrix Multiplication with CUDA --- A basic introduction to the CUDA programming model,” by Robert Hochberg

29. “High-Performance Computing Systems and Our Future”
CAPPLab Research Activities
Asaduzzaman, A., Gummadi, D., and Yip, C.M., “A Talented CPU-to-GPU Memory Mapping Technique,” in IEEE SoutheastCon 2014, Lexington, KY, March 13-16, 2014.
Asaduzzaman, A., Yip, C.M., Kumar, S., and Asmatulu, R., “Fast, Effective, and Adaptable Computer Modeling and Simulation of Lightning Strike Protection on Composite Materials,” in IEEE SoutheastCon Conference 2013, Jacksonville, FL, April 4-7, 2013.
Asaduzzaman, A., Mitra, P., Chidella, K.K., Saeed, K.A., Cluff, K., and Mridha, M.F., “A Computer-Assisted Mammography Technique for Analyzing Breast Cancer,” IEEE International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, Sept , 2017.

30. “High-Performance Computing Systems and Our Future”
CAPPLab Research Activities (+)
Asaduzzaman, A., Chidella, K.K., and Vardha, D., “An Energy-Efficient Directory Based Multicore Architecture with Wireless Routers to Minimize the Communication Latency,” IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 28, No. 2, pp , May 2016.
… Distributed Directory … CAEE Journal, under review, Nov
Chidella, K.K. and Asaduzzaman, A., “A Novel Directory Based WNoC-GPU System to Enhance Performance,” under preparation, 2017.

31. “High-Performance Computing Systems and Our Future”
CAPPLab Research Activities (+)
Research Grants/Awards
( ) “High Performance Computing at Low (Room) Temperature,” CybertronPC; total $10,000 for one year (includes hardware donation).
( ) “NVIDIA GPU Research Center at Wichita State,” NVIDIA Corporation; total $10,000 for two years (includes hardware donation).
(2015–2016) “Collaborative Research: NetApp NFS Connector for Apache Spark Systems,” NetApp, Inc.; total $60,000 for nine months.
(2014–2015) “An Empirical Application of High-Performance Pattern Recognition and Protein Binding to Treat Cancer,” WSU Flossie E. West Memorial Foundation; total $24,984 for one year.
(2014–2015) “Discovering CUDA-Accelerated New Programming Paradigm to Address the Growing Low-Power High Performance Computing Requirements,” WSU URCP Award; total $4,498 for one year.
(2014–2015) “Xilinx University Program (XUP) Award;” Xilinx; total $1,644 (hardware donation).
(2014–2014) “Wiktronics-WSU Embedded Systems Research Project 2014,” Wiktronics Collaborative Project 2014; total $11,466 for six months.
(2013–2014), “A novel task and data regrouping based parallel approach to solve massive problems faster on multithreaded computing systems,” Kansas NSF EPSCoR First Award; total $105,296 for 15 months.
(2012–2012) “M2SYS-WSU Biometric Cloud Computing Research Project;” M2SYS Technology; total $2,875 for four months.

33. “High-Performance Computing Systems and Our Future”
Outline ►
Introduction
We and Our Future
High-Performance Computing (HPC) Activities
HPC Systems
Computer Systems
SMT-Capable CPU-GPU Systems
Examples: HPC Systems for Big Data
Matrix Multiplication, Graph
CAPPLab Research
Concluding Remarks

34. “High-Performance Computing Systems and Our Future”
Concluding Remarks
Understanding the world’s biggest mysteries (such as human genome and climate change) would be impossible without computers.
Supercomputers operates at Peta (10^15) FLOPS; however, require tens of millions of dollars.
High-performance computing (HPC) offers similar processing speed (10^12 FLOPS) with more affordability, accessibility, and ease-of-use.
This presentation introduced HPC systems and examples how HPC solves big data problems effectively, and discussed some societal benefits of research on HPC systems.
Computing, Sensing, Communicating, Monitoring, and Controlling (CSCMC) – all-in-one for future computing!!! [1]
[1] The Future of Computing Performance: Game Over or Next Level? by Fuller and Millett, NAP (2011),

35. Thank You! IEEE-HKN Chapter at Wichita State QUESTIONS/COMMENTS?
“High-Performance Computing Systems and Our Future”
QUESTIONS/COMMENTS?
Contact: Abu Asaduzzaman
Phone:
Thank You!