CS 277 Data Centric Computer Architecture, Winter 2023
Instructor: Elaheh Sadredini, elaheh@cs.ucr.edu
Time: Monday/Wednesday 12:00 pm to 1:20 pm
Office hours: by appointment
Course Description
Many modern and important workloads, including machine learning, databases, computational biology, graph processing, and real-time data analytics have irregular memory access patterns, relatively low data reuse, low cache line utilization, low computational intensity, and large datasets that greatly exceed the main memory size. Processing and analyzing large data sets drives the demand for more computation and puts even larger demand on the memory and storage infrastructure. At the same time, the performance gap between processing units and memory units is increasing, and this is a major source of performance and energy bottleneck for memory-bound applications. Enabling the next generation of data-intensive applications requires computing to be embedded near the data, which is often called near/in-memory processing.
In this course, we will cover fundamental and state-of-the-art research in emerging trends in computer architecture, such as near-memory/in-memory computing architectures and the proposed near-data accelerators for key applications like databases, machine learning, computational biology, graph processing, and pattern matching. You will also work with the first real-world processing-in-memory (PIM) architecture. The course will include paper presentations, class discussions, and a final project. You can potentially work on developing and optimizing new workloads for the first real-world PIM hardware or explore new near-memory and in-memory designs in simulators, or do something else that can advance our understanding of the processing in/near-memory paradigm.
Course Prerequisite(s)
- Digital design and computer architecture (or equivalent course)
- Familiarity with C/C++ programming
We will be using Piazza (find the link in Canvas) as our class forum, and our primary way of communication outside of class. All general inquiries must be made on Piazza. For group-specific questions or private questions, you can either email me or post a private question on Piazza.
Lecture presentations and other course materials will be posted in Canvas.
Grading and Policies
Grading Breakdown
- Paper persentation: 15%
- Paper review: 20%
- Class participation and discussion: 10%
- Final project: 60% (up to 5% bonus)
Grading Policies
Report format: Project reports are to be typeset in LaTeX using the ISCA 2020 template.
Late submission: We do not accept any late submission for paper reviews and reports.
Attendance: You are expected to attend all lectures. For this course, discussion is essential!
Dues: All assignments are due at 11:59pm of the submission deadline.
Cheating policy: Working with others on assignments/projects is a good way to learn the material and is encouraged. However, there are limits to the degree of cooperation that is permitted. Students may discuss among themselves the meaning of homework problems and possible approaches to solving them. Any written portion of an assignment, however, is to be done strictly on an individual basis. You may not copy from another student or from any other source, and you may not allow another student to copy your work. Any violation of the above is considered to be cheating and will result in a failing grade in the class (no exceptions).
Errors in grading: If you feel there has been an error in how an assignment or test was graded, you have one week from when the assignment is returned to bring it to our attention. You must submit (via email to the instructor) a written description of the problem. We will not discuss regrades without receiving an email from you about it first.
Grades: Your score will be available on Canvas.
Grading Scale
A+: 96 and above
A: 93-95.9
A-: 90-92.9
B+: 87-89.9
B: 83-86.9
B-: 80-82.9
C+: 77-79.9
C: 73-76.9
C-: 70-72.9
D+: 67-69.9
D: 63-66.9
D-: 60-62.9
F: Below 60
Academic Integrity
Here at UCR we are committed to upholding and promoting the values of the Tartan Soul: Integrity, Accountability, Excellence, and Respect. As a student in this class, it is your responsibility to act in accordance with these values by completing all assignments in the manner described, and by informing the instructor of suspected acts of academic misconduct by your peers. By doing so, you will not only affirm your own integrity, but also the integrity of the intellectual work of this University, and the degree which it represents. Should you choose to commit academic misconduct in this class, you will be held accountable according to the policies set forth by the University, and will incur appropriate consequences both in this class and from Student Conduct and Academic Integrity Programs. For more information regarding University policy and its enforcement, please visit: http://conduct.ucr.edu.
Student Resources
◆ If you need special accomodation, please either contact me or find more information here: "UCR campus resources"
Course Schedule
Link to the tentative schedule, reading material, and due dates
Paper Presentation and Review
Groups and Projects
TBDResources
◆ WWW Computer Architecture Home Page: a comprehensive guide to research, tools, and general information on computer architecture.
◆ Innovations in the Memory System: Rajeev Balasubramonian, Synthesis Lectures on Computer Architecture , Morgan and Claypool Publishers, 2019.
◆ Memory Systems: Cache, DRAM, Disk: Bruce Jacob, David T. Wang, and Spencer Ng, Morgan Kaufmann, 2007.