Page last updated on 2020 March 16

*Enrollment code:* 13508

*Prerequisite:* ECE 254A (can be waived, but ECE 154B is required)

*Class meetings:* MW 10:00-11:30, Phelps 1431

*Instructor:* Professor Behrooz Parhami

*Open office hours:* M 12:00-2:00, W 1:00-2:00, HFH 5155

**Course announcements:** Listed in reverse chronological order

**Course calendar:** Schedule of lectures, homework, exams, research

**Homework assignments:** Eight assignments, worth a total of 30%

**Exams:** Closed-book midterm, 30%; Closed-book final, 40%

**Research paper:** No research paper for winter 2020

**Research paper guidlines:** Brief guide to format and contents (N/A)

**Poster presentation tips:** Brief guide to format and structure (N/A)

**Policy on academic integrity:** Please read very carefully

**Grade statistics:** Range, mean, etc. for homework and exam grades

**References:** Textbook and other sources (Textbook's web page)

**Lecture slides:** Available on the textbook's web page

**Miscellaneous information:** Motivation, catalog entry, history

Note that campus policies do allow holding an in-person final exam, if needed. Following one of the alternate recommendations for final exams from the campus administration, I have decided to make the in-person final exam optional. If you don't take the exam, your grade will be determined based on course assessments so far (homework assignements and midterm exam).

Everyone will be assigned a grade based on the alrealy-completed work. Taking the final exam can only raise your grade, not lower it. I hope this method alleviates your concerns about appearing in our smallish classroom with a number of others to take the final exam. The decision whether to take the final exam is now yours.

Winter is also the quarter during which our campus engages in "UCSB Reads," a program that began in 2007 and has chosen the book

Accordingly, I have decided to use four micro-projects as part of homework assignments for ECE 254B during winter 2020. The course's students will get free copies of

uProject A: From Weather Forecasting to Climate Modeling

uProject B: Ocean-Temperature Modeling: Monster Storms

uProject C: Modeling of Sea-Level Rise: Disappearing Lands

uProject D: Extreme-Weather Projections from Climate Data

For each micro-project, you will study the types of computer models involved, computational requirements of the models, how the computations are performed on top-of-the-line supercomputers, and data sets that allow drawing various conclusions from the modeling results.

Course lectures and homework assignments have been scheduled as follows. This schedule will be strictly observed. In particular, no extension is possible for homework due dates; please start work on the assignments early. Each lecture covers topics in 1-2 chapters of the textbook. Chapter numbers are provided in parentheses, after day & date. PowerPoint and PDF files of the lecture slides can be found on the textbook's web page.

**Day & Date (book chapters) Lecture topic [Homework posted/due] {Special notes}**

M 01/06 (1) Introduction to parallel processing

W 01/08 (2) A taste of parallel algorithms [HW1 posted; uProject A]

M 01/13 (3-4) Complexity and parallel computation models

W 01/15 (5) The PRAM shared-memory model and basic algorithms [HW1 due] [HW2 posted; chs. 1-4]

M 01/20 No lecture: Martin Luther King Holiday

W 01/22 (6A) More shared-memory algorithms [HW2 due] [HW3 posted, uProject B]

M 01/27 (6B-6C) Shared memory implementations and abstractions

W 01/29 (7) Sorting and selection networks [HW3 due] [HW4 posted; chs. 5-7]

M 02/03 (8A) Search acceleration circuits

W 02/05 (8B-8C) Other circuit-level examples [HW4 due] [HW5 posted; uProject C]

M 02/10 (1-7) Closed-book midterm exam {10:00-11:45 AM}

W 02/12 (9) Sorting on a 2D mesh or torus architectures [HW5 due]

M 02/17 No lecture: President's Day Holiday

W 02/19 (10) Routing on a 2D mesh or torus architectures [HW6 posted; chs. 8A-10]

M 02/24 (11-12) Other mesh/torus concepts

W 02/26 (13) Hypercubes and their algorithms [HW6 due] [HW7 posted; uProject D]

M 03/02 (14) Sorting and routing on hypercubes

W 03/04 (15-16) Other interconnection architectures [HW7 due] [HW8 posted; chs. 11-16]

M 03/09 (17) Emulation and task scheduling {Instructor/course evaluation surveys}

W 03/11 (18-19) Input/output and reliability considerations [HW8 due] {Class cancelled; see announcements}

M 03/16 (8A-19) Closed-book final exam {In our regular classroom, 8:30-11:00 AM}

T 03/25 {Course grades due by midnight}

- Because solutions will be handed out on the due date, no extension can be granted.

- Include your name, course name, and assignment number at the top of the first page.

- If homework is handwritten and scanned, make sure that the PDF is clean and legible.

- Although some cooperation is permitted, direct copying will have severe consequences.

** Homework 1: Micro-Project A** (due W 2020/01/15, 10:00 AM)

Numerical weather prediction has a long history. As noted at the beginning of Section 1.3 of our textbook, British meteorologist Lewis Fry Richardson formulated a vision for using a large number of "computers" (humans, with mechanical calculators) to speed up the required calculations. Now, fast processors can do a decent job of running weather models and many thousands of processors can be used to perform the calculations required by more sophisticated models within hours, not weeks or months.

Your assignment is to prepare a single-spaced typed report (12-point font, 3 pages max, including figures and references) that discusses the computational requirements of modern weather prediction models, as well as models for climate forecasting, enumerating the differences between the two kinds of models in terms of the data they use, prediction time-frames, and the kinds of calculations involved. How does the availability of exascale computers help improve accuracy and execution speed for these models?

Here is a list of references for uProject A, collected mostly from your submitted project reports.

[Baue15] P. Bauer, A. Thorpe, and G. Brunet, "The quiet revolution of numerical weather prediction," *Nature*, Vol. 525, No. 7567, pp. 47-55, 2015.

[Boyd12] E. Boyd and E. L. Tompkins, *Climate Change: A Beginner's Guide*, Oneworld Publications, 2012.

[Char50] J. G. Charney, R. Fjortoft, and J. von Neumann, "Numerical integration of the barotropic vorticity equation," *Tellus*, Vol. 2, No. 4, pp. 237-254, 1950.

[Fuhr17] O. Fuhrer et al., "Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0," *Geoscientific Model Development*, Vol. 11, No. 4, pp. 1665-1681, 2018.

[Inno17] M. E. Innocenti et al., "Progress towards physics-based space weather forecasting with exascale computing," *Advances in Engineering Software*, Vol. 111, pp. 3-17, 2017.

[Lync08] P. Lynch, "The origins of computer weather prediction and climate modeling," *J. Computational Physics*, Vol. 227, No. 7, pp. 3431-3444, 2008.

[Mait13] S. Maity, S. Bonthu, K. Sasmal, and H. Warrior, "Role of parallel computing in numerical weather forecasting models," *Int'l J. Computer Applications*, Vol. 4, pp. 22-27, 2013.

[Marr15] S. Marras et al., "A review of element-based Galerkin methods for numerical weather prediction: Finite elements, spectral elements, and discontinuous Galerkin," *Archives of Computational Methods in Engineering*, Vol. 23, No. 4, pp. 673-722, 2016.

[Mull14] E. H. Muller and R. Scheichl, "Massively parallel solvers for elliptic partial differential equations in numerical weather and climate prediction," *Quarterly J. Royal Meteorological Society*, Vol. 140, No. 685, pp. 2608-2624, 2014.

[NRC12] National Research Council, *A National Strategy for Advancing Climate Modeling*, National Academies Press, 2012.

[Schu18] T. C. Schulthess et al., "Reflecting on the goal and baseline for Exascale Computing: A roadmap based on weather and climate simulations," *Computing in Science & Engineering*, Vol. 21, No. 1, pp. 30-41, 2018.

[Slin08] J. Slingo et al., "Developing the next-generation climate system models: challenges and achievements," *Philosophical Trans. Royal Society—A: Mathematical, Physical and Engineering Sciences*, Vol. 367, No. 1890, pp. 815-831, 2008.

** Homework 2: Introduction, complexity, and models** (chs. 1-4, due W 2020/01/22, 10:00 AM)

Do the following problems from the textbook: 1.10, 2.9, 3.7cd, 4.16 (defined below)

One of the approaches proposed for overcoming the memory wall is "in-memory" (or "near-memory") computation. Read the paper [Ali20] and answer the following questions.

a. What is "in-memory computing" (define precisely) and why is it useful?

b. Why opt for adding numbers bit-serially, the slowest addition scheme?

c. Can the methods discussed be extended to other arithmetic operations?

[Ali20] M. F. Ali, A. Jaiswal, and K. Roy, "In-Memory Low-Cost Bit-Serial Addition Using Commodity DRAM Technology,"

** Homework 3: Micro-Project B** (due W 2020/01/29, 10:00 AM)

An aspect of climate and long-term weather modeling is predicting ocean temperatures, as briefly discussed on p. 7 of our textbook. One might think that the oceans all being connected to one another means that temperature should stabilize after a while to a common global ocean temperature. This is far from being the case, as this world-sea-temperatures map indicates.

So, a key question is: How do we go about predicting ocean temperatures in a decade? In 20 years? In 50 years? This is important, because ocean temperatures have a direct impact on the number and intensity of hurricanes and other storms, and they also affect weather phenomena more generally.

Your assignment is to prepare a single-spaced typed report (12-point font, 3 pages max, including figures and references) that discusses the computational requirements of ocean-temperatures forecasting models, the kinds of calculations involved, and trade-offs between model accuracy and computation time. [Link 1] [Link 2]

[Wand19] N. Wanders, M. T. H. van Vliet, Y. Wada, M. F. P. Bierkens, and L. P. H. van Beek, "High-Resolution Global Water Temperature Modeling,"

Here is a list of references for uProject B, collected mostly from your submitted project reports.

[Abra13] J. P. Abraham et al., "A Review of Global Ocean Temperature Observations: Implications for Ocean Heat Content Estimates and Climate Change," *Reviews of Geophysics*, Vol. 51, No. 3, pp. 450-483, 2013.

[Harv19] C. Harvey, "Oceans Are Warming Faster than Predicted," *Scientific American*, E&E News Climate, January 2019.

[OCar19] A. G. O'Carroll et al., "Observational Needs of Sea Surface Temperature," *Frontiers in Marine Science*, Vol. 6, Article 420, 2019.

[vanB12] L. P. H. van Beek et al., "A Physically Based Model of Global Freshwater Surface Temperature," *Water Resources Research*, Vol. 48, No. 9, 2012.

[Ward06] B. Ward, "Near-Surface Ocean Temperature," *J. Geophysical Research: Oceans*, Vol. 111, No. C2, February 2006.

[Zann19] L. Zanna, S. Khatiwala, J. Gregory, J. Ison, and P. Heimbach, "Global Reconstruction of Historical Ocean Heat Storage and Transport," *Proc. National Academy of Sciences*, Vol. 116, No. 4, pp. 1126-1131, 2019.

** Homework 4: Shared memory and sorting networks** (chs. 5-7, due W 2020/02/05, 10:00 AM)

Do the following problems from the textbook: 5.12, 6.13, 7.9, 16.20ab (defined below)

Consider a Clos network with

for all

out(0,

out(1,

a. Prove that the Clos network can realize any

b. If the cost of an

** Homework 5: Micro-Project C** (due W 2020/02/12, 10:00 AM)

Sea levels rise by three distinct mechanisms: (1) Thermal expansion; (2) Increase in water mass; (3) Depth changes due to movements in the Earth's crust. Predicting sea-level rise is important. Entire island nations will disappear with a rise of only a few feet. Other nations will lose low-lying coastal lands, which are usually densely-populated regions. New York and 16 other US cities will suffer significant displacements and property loss with just a 10-foot rise in sea level (some projections go well beyond 10 feet). Stories from some of these endangered US coastal regions are covered in the "UCSB Reads" book,

Your assignment is to prepare a single-spaced typed report (12-point font, 3 pages max, including figures and references) that discusses the computational requirements of sea-level rise models, the kinds of calculations involved, sources of uncertainty in the predictions, and probabilistic resolution of such uncertainties.

[Swee17] W. V. Sweet, R. Horton, R. E. Kopp, A. N. LeGrande, and A. Romanou, "Sea Level Rise,"

Here is a list of references for uProject C, collected mostly from your submitted project reports.

[Doyl15] T. W. Doyle, B. Chivoiu, and N. M. Enwright, *Sea-Level Rise Modeling Handbook—Resource Guide for Coastal Land Managers, Engineers, and Scientists*, US Geological Survey Professional Paper 1815, 76 pp., 2015.

[Hay15] C. C. Hay et al., "Probabilistic Reanalysis of Twentieth-Century Sea-Level Rise," *Nature*, Vol. 517, No. 7535, pp. 481-484, 2015.

[Jevr12] S. Jevrejeva et al., "Sea Level Projections to AD2500 with a New Generation of Climate Change Scenarios," *Global and Planetary Change*, Vol. 80, pp. 14-20, January 2012.

[Leve13] A. Levermann, P. U. Clark, B. Marzeion, G. A. Milne, D. Pollard, V. Radic, and A. Robinson, "The Multimillennial Sea-Level Commitment of Global Warming," *Proc. National Academy of Sciences*, Vol. 110, No. 34, pp. 13745-13750, August 2013.

[Lind19] R. Lindsey, "Climate Change: Global Sea Level," Article posted on Climate.gov, NOAA, November 2019.

[Rahm07] S. Rahmstorf, "A Semi-Empirical Approach to Projecting Future Sea-Level Rise," *Science*, Vol 315, No. 5810, pp. 368-370, January 2007.

[Rahm12] S. Rahmstorf, "Modeling Sea Level Rise", *Nature Education Knowledge*, Vol. 3, No. 10, 2012.

[Viss15] H. Visser, S. Dangendorf, and A. C. Petersen, "A Review of Trend Models Applied to Sea Level Data with Reference to the 'Acceleration-Deceleration Debate'," *J. Geophysical Research: Oceans*, Vol. 120, pp. 3873-3895, June 2015.

** Homework 6: Circuits and mesh/torus networks** (chs. 8A-10, due W 2020/02/26, 10:00 AM)

Do the following problems from the textbook: 8.8, 9.7, 10.14abc, 10.19 (defined below)

Show that on an

** Homework 7: Micro-Project D** (due W 2020/03/04, 10:00 AM)

The term "extreme weather" refers to intense heat/cold waves, widespread floods, prolonged droughts, severe winds, and the like. An analogy with performance records in sports and other domains might be helpful. Over time, sports records improve, because of enhanced techniques and better training, as well as random variations. However, there are factors at play that also lead to more frequent breaking of records. Examples include better equipment (e.g., soccer balls, running shoes, or baseball bats), which are sometimes viewed as giving modern atheletes an unfair edge. In the domain of music, record sales provide another example, where higher sales figures do not necessarily mean better music. In extreme-weather modeling, too, we look for underlying factors beyond random variations. Predicting droughts in California is one of the important areas of focus in the US.

Your assignment is to prepare a single-spaced typed report (12-point font, 3 pages max, including figures and references) that discusses the computational requirements of extreme-weather projection models, the kinds of calculations involved, and how the models tie in with those of the previous three micro-projects.

[Pete13] T. C. Peterson et al., "Monitoring and Understanding Changes in Heat Waves, Cold Waves, Floods and Droughts in the United States: State of Knowledge,"

Here is a list of references for uProject D, collected mostly from your submitted project reports.

[Hao18] Z. Hao, V. P. Singh, and Y. Xia, "Seasonal Drought Prediction: Advances, Challenges, and Future Prospects," *Reviews of Geophysics*, Vol. 56, No. 1, pp. 108-141, 2018.

[Lubc12] J. Lubchenco and T. R. Karl, "Predicting and Managing Extreme Weather Events," *Physics Today*, Vol. 65, No. 3, p. 31, 2012.

[Meeh00] G. A. Meehl, F. Zwiers, J. Evans, T. Knutson, L. Mearns, and P. Whetton, "Trends in Extreme Weather and Climate Events: Issues Related to Modeling Extremes in Projections of Future Climate Change," *Bull. American Meteorological Society*, Vol. 81, No. 3, pp. 427-436, 2000.

[Sill17] J. Sillmann et al., "Understanding, Modeling and Predicting Weather and Climate Extremes: Challenges and Opportunities," *Weather and Climate Extremes*, Vol. 18, pp. 65-74, 2017.

[Vavr15] S. J. Vavrus, M. Notaro, and D. J. Lorenz, "Interpreting Climate Model Projections of Extreme Weather Events," *Weather and Climate Extremes*, Vol. 10, pp. 10-28, 2015.

[Zwie13] F. W. Zwiers, L. V. Alexander, G. C. Hegerl, T. R. Knutson, J. P. Kossin, P. Naveau, N. Nicholls, C. Schar, S. I. Seneviratne, and X. Zhang, "Climate Extremes: Challenges in Estimating and Understanding Recent Changes in the Frequency and Intensity of Extreme Climate and Weather Events," in *Climate Science for Serving Society*, Springer, 2013, pp. 339-389.

** Homework 8: Hypercubic and other architectures** (chs. 11-16, due W 2020/03/11, 10:00 AM)

Do the following problems from the textbook: 12.24 (defined below), 13.14, 15.3, 16.2

Read the paper [Andu15] about

a. Describe the proposed topology in no more than two sentences.

b. What is the main advantage of twin torus over ordinary torus with the same number of nodes?

c. What is a primary disadvantage of twin torus compared with ordinary torus? Explain.

d. Propose a generalization to triplet torus topology and cite its advantages and disadvantages.

[Andu15] Andujar-Munoz, F. J., J. A. Villar-Ortiz, J. L. Sanchez, F. J. Alfaro, and J. Duato, "

The following sample exams using problems from the textbook are meant to indicate the types and levels of problems, rather than the coverage (which is outlined in the course calendar). Students are responsible for all sections and topics, in the textbook, lecture slides, and class handouts, that are not explicitly excluded in the study guide that follows the sample exams, even if the material was not covered in class lectures.

** Sample Midterm Exam (105 minutes)** (Chapters 8A-8C do not apply to this year's midterm)

Textbook problems 2.3, 3.5, 5.5 (with

*Midterm Exam Study Guide*

The following sections are excluded from Chapters 1-7 of the textbook to be covered in the midterm exam, including the three new chapters named 6A-C (expanding on Chpater 6):

3.5, 4.5, 4.6, 6A.6, 6B.3, 6B.5, 6C.3, 6C.4, 6C.5, 6C.6, 7.6

** Sample Final Exam (150 minutes)** (Chapters 1-7 do not apply to this year's final)

Textbook problems 1.10, 6.14, 9.5, 10.5, 13.5a, 14.10, 16.1; note that problem statements might change a bit for a closed-book exam.

*Final Exam Study Guide*

The following sections are excluded from Chapters 8A-19 of the textbook to be covered in the final exam:

8A.5, 8A.6, 8B.2, 8B.5, 8B.6, 9.6, 11.6, 12.5, 12.6, 13.5, 15.5, 16.5, 16.6, 17.1, 17.2, 17.6, 18.6, 19

**[Not for winter 2020]** Our research focus this quarter will be on the topical area of big data, as defined in class and the reading/research assignments for the first three course homeworks. We will study the intersection of big data with high-performance computing, that is, how the meteoric spread of big-data applications affects the field of parallel/distributed computing and how research in our field can help solve the challenges brought about by big data.

The "4 Vs" of big data are nicely illustrated in the following IBM infographic. The fourth "V," equated in the image with "veracity," having to do with correctness and accuracy of data, is sometimes replaced by "value" (see the ssecond homework assignment), the data's importance or worth to an enterprise or application. Alternatively, one might view the big-data domain as being defined by "5 Vs."

Here is a list of research paper titles. I am posting the list, even though some of the info is incomplete, in order to give you a head start on choosing a topic. In topic selection, you can either propose a topic of your choosing, which I will review and help you refine into something that would be manageable and acceptable for a term paper, or you can give me your first to third choices among the topics that follow. I will then assign a topic to you by February 12, based on your preferences and those of your classmates. Sample references follow the titles to help define the topics and serve as starting pointa for your study.

1. Hybrid Disk Drives for Big-Data Storage Needs (Assigned to: )

[Wu09] X. Wu and A. L. Narasimha Reddy, "Managing Storage Space in a Flash and Disk Hybrid Storage System," *Proc. IEEE Int'l Symp. Modeling, Analysis, and Simulation of Computer and Telecommunication Systems*, 2009, pp. 1-4.

Search terms: Flash disk cache

2. GPU-Based Parallel Architectures for Big Data (Assigned to: )

[Liu13] Z. Liu, B. Jiang, and J. Heer, "imMens: Real-Time Visual Querying of Big Data," *Computer Graphics Forum*, Vol. 32, No. 3, 2013, pp. 421-430.

Search terms: GPU-based supercomputing

3. Network Coding for Fast Exchange of Big Data (Assigned to: )

[Dima10] A. G. Dimakis, P. B. Godfrey, Y. Wu, M. J. Wainwright, and K. Ramchandran, "Network Coding for Distributed Storage Systems," *IEEE Trans. Information Theory*, Vol. 56, No. 9, 2010, pp. 4539-4551.

Search terms:

4. Big-Data Allocation and Load-Balancing Methods (Assigned to: )

[Wang14] K. Wang, X. Zhou, T. Li, D. Zhao, M. Lang, and I. Raicu, "Optimizing Load Balancing and Data-Locality with Data-Aware Scheduling," *Proc. IEEE Int'l Conf. Big Data*, 2014, pp. 119-128.

Search terms:

5. Low-Redundancy Methods for Big-Data Protection (Assigned to: )

[Amja12] T. Amjad, M. Sher, and A. Daud, "A Survey of Dynamic Replication Strategies for Improving Data Availability in Data Grids," *Future Generation Computer Systems*, Vol. 28, No. 2, 2012, pp. 337-349.

Search terms:

6. RAID in the Era of Cloud-Based Big-Data Storage (Assigned to: )

[Gu14] M. Gu, X. Li, and Y. Cao, "Optical Storage Arrays: A Perspective for Future Big Data Storage," *Light: Science & Applications*, Vol. 3, No. 5, 2014.

Search terms:

7. Structural Representations for Big-Data Storage (Assigned to: )

[Sand14] A. Sandryhaila and J. M. F. Moura, "Big Data Analysis with Signal Processing on Graphs: Representation and Processing of Massive Data Sets with Irregular Structure," *IEEE Signal Processing*, Vol. 31, No. 5, 2014, pp. 80-90.

Search terms:

8. Meeting Reliability Requirements of Big Data (Assigned to: )

[Sath13] M. Sathiamoorthy, M. Asteris, D. Papailiopoulos, A. G. Dimakis, R. Vadali, S. Chen, and D. Borthakur, "XORing Elephants: Novel Erasure Codes for Big Data," *Proc. Symp. VLDB*, Vol. 6, No. 5, 2013, pp. 325-336.

Search terms: Reliable distributed systems; Data integrity

9. Search and Query Optimizations for Big Data (Assigned to: )

[Du92] W. Du, R. Krishnamurthy, and M.-C. Shan. "Query Optimization in a Heterogeneous DBMS," *Proc. Symp. VLDB*, 1992, Vol. 92, pp. 277-291.

Search terms:

10. The MapReduce Paradigm for Big-Data Parallelism (Assigned to: )

[Dean10] J. Dean and S. Ghemawat, "MapReduce: A Flexible Data Processing Tool," *Communications of the ACM*, Vol. 53, No. 1, 2010, pp. 72-77.

Search terms: Data-parallel computing; Divide-and-conquer paradigm

11. Hadoop's Distributed File System for Big Data (Assigned to: )

[Shva10] K. Shvachko, H. Kuang, S. Radia, and R. Chansler, "The Hadoop Distributed File System," *Proc. 26th IEEE Symp. Mass Storage Systems and Technologies*, 2010, pp. 1-10.

Search terms: Hadoop; Distributed file systems; Distributed databases

12. Big-Data Longevity and Compatibility Challenges (Assigned to: )

[Garf04] T. Garfinkel, B. Pfaff, J. Chow, and M. Rosenblum, "Data Lifetime Is a Systems Problem," *Proc. 11th ACM SIGOPS European Workshop*, 2004, p. 10.

Search terms: Data preservation; Long-term data archiving

13. Advances in Intelligent Systems with Big Data (Assigned to: )

[OLea13] D. E. O'Leary, "Artificial Intelligence and Big Data," *IEEE Intelligent Systems*, Vol. 28, No. 2, 2013, pp. 96-99.

Search terms: Data-driven AI; Large training data-sets

14. FPGA-Based Accelerators for Big-Data Computing (Assigned to: )

[Wang15] C. Wang, X. Li, and X. Zhou, "SODA: Software Defined FPGA Based Accelerators for Big Data," *Proc. Design Automation & Test Europe Conf.*, 2015, pp. 884-887.

Search terms: Hardware accelerators; Application-specific coprocessors

15. Big-Data Aspects of Distributed Sensor Networks (Assigned to: )

[Taka14] D. Takaishi, H. Nishiyama, N. Kato, and R. Miura, "Toward Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks," *IEEE Trans. Emerging Topics in Computing*, Vol. 2, No. 3, pp. 388-397, 2014.

Search terms: Ad-hoc networks; Data storage in sensor networks

16. Big-Data Challenges of Maps and Auto-Navigation (Assigned to: )

[Shek12] S. Shekhar, V. Gunturi, M. R. Evans, and K. Yang, "Spatial Big-Data Challenges Intersecting Mobility and Cloud Computing." *Proc. 11th ACM Int'l Workshop Data Engineering for Wireless and Mobile Access*, 2012, pp. 1-6.

Search terms: Geographic information systems; GPS navigation

17. Shared-Memory Models for Big-Data Applications (Assigned to: )

[Bell18] C. G. Bell and I. Nassi, "Revisiting Scalable Coherent Shared Memory," *IEEE Computer*, Vol. 51, No. 1, pp. 40-49, January 2018.

Search terms: Memory consistency; Distributed shared memory; Directory-based coherence

18. Edge Computing for Big Data (Assigned to: )

[Shar17] S. K. Sharma and X. Wang, "Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks," *IEEE Access*, Vol. 5, pp. 4621-4635, 2017.

**[Not for winter 2020]** Here are some guidelines for preparing your research poster. The idea of the poster is to present your research results and conclusions thus far, get oral feedback during the session from the instructor and your peers, and to provide the instructor with something to comment on before your final report is due. Please send a PDF copy of the poster via e-mail by midnight on the poster presentation day.

Posters prepared for conferences must be colorful and eye-catching, as they are typically competing with dozens of other posters for the attendees' attention. Here is an example of a conference poster. Such posters are often mounted on a colored cardboard base, even if the pages themselves are standard PowerPoint slides. In our case, you should aim for a "plain" poster (loose sheets, to be taped to the wall in our classroom) that conveys your message in a simple and direct way. Eight to 10 pages, each resembling a PowerPoint slide, would be an appropriate goal. You can organize the pages into 2 x 4 (2 columns, 4 rows), 2 x 5, or 3 x 3 array on the wall. The top two of these might contain the project title, your name, course name and number, and a very short (50-word) abstract. The final two can perhaps contain your conclusions and directions for further work (including work that does not appear in the poster, but will be included in your research report). The rest will contain brief description of ideas, with emphasis on diagrams, graphs, tables, and the like, rather than text which is very difficult to absorb for a visitor in a very limited time span.

HW1 grades: Range = [B+, A+], Mean = 3.8, Median = A–

HW2 grades: Range = [B, A+], Mean = 3.7, Median = A–

HW3 grades: Range = [B+, A+], Mean = 3.8, Median = A–

HW4 grades: Range = [B+, A+], Mean = 3.7, Median = A–

HW5 grades: Range = [B+, A+], Mean = 3.8, Median = A–

HW6 grades: Range = [C–, A+], Mean = 3.6, Median = A–

HW7 grades: Range = [B, A], Mean = 3.7, Median = A–

HW8 grades: Range = [D+, A+], Mean = 3.5, Median = A–

Overall homework grades (percent): Range = [00, 00], Mean = 00, Median = 00

Midterm exam grades (percent): Range = [53, 97], Mean = 75, Median = 73

Optional final exam grades (percent): Range = [34, 79], Mean = 58, Median = 59

Course letter grades: Range = [B, A+], Mean = 3.6, Median = A–

** Required text:** B. Parhami,

The follolwing journals contain a wealth of information on new developments in parallel processing:

The following are the main conferences of the field: Int'l Symp. Computer Architecture (ISCA, since 1973), Int'l Conf. Parallel Processing (ICPP, since 1972), Int'l Parallel & Distributed Processing Symp. (IPDPS, formed in 1998 by merging IPPS/SPDP, which were held since 1987/1989), and ACM Symp. Parallel Algorithms and Architectures (SPAA, since 1988).

UCSB library's electronic journals, collections, and other resources

** Motivation:** The ultimate efficiency in parallel systems is to achieve a computation speedup factor of

*Catalog entry:* 254B. Advanced Computer Architecture: Parallel Processing(4) PARHAMI.*Prerequisites: ECE 254A. Lecture, 4 hours*. The nature of concurrent computations. Idealized models of parallel systems. Practical realization of concurrency. Interconnection networks. Building-block parallel algorithms. Algorithm design, optimality, and efficiency. Mapping and scheduling of computations. Example multiprocessors and multicomputers.

** History:** The graduate course ECE 254B was created by Dr. Parhami, shortly after he joined UCSB in 1988. It was first taught in spring 1989 as ECE 594L, Special Topics in Computer Architecture: Parallel and Distributed Computations. A year later, it was converted to ECE 251, a regular graduate course. In 1991, Dr. Parhami led an effort to restructure and update UCSB's graduate course offerings in the area of computer architecture. The result was the creation of the three-course sequence ECE 254A/B/C to replace ECE 250 (Adv. Computer Architecture) and ECE 251. The three new courses were designed to cover high-performance uniprocessing, parallel computing, and distributed computer systems, respectively. In 1999, based on a decade of experience in teaching ECE 254B, Dr. Parhami published the textbook

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