Welcome to Undergraduate Research!
The class webpage is located at https://www.cs.ucr.edu/~mchow009/teaching/cs193/spring2021
Information, resources, and announcements related to the class will be posted to the webpage.
This Design Project course will focus on the development and evaluation of Basic Linear Algebra Subroutine (BLAS) Libraries on several parallel computing architectures. Specifically, this project will utilize Nvidia’s Jetson Nano, AMD GPU's, the Raspberry Pi, and Bespoke Silicon Group’s Manycore processor. The goal of this project is to survey each system for its performance and power consumption in order to explore potential energy-efficiency benefits. This will enable accessible, energy efficient embedded systems to perform computationally complex functions, while being able to scale both utilization and power consumption proportionally
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Ana Cardenas Beltran
HammerBlade is Single-Program, Multiple-Data (SMPD) architecture. It is architected from an array of tiles that are interconnected by the manycore accelerator network. HammerBlade is a new architecture tshat we have never dealt with, so our goal for the Spring 2021 quarter is to understand the HammerBlade’s architecture and learn how to program using CUDA-Lite. While we did have experience with programming in C/C++ and a few other languages when we were first introduced to CUDA-Lite, we did not have much experience with the Linux OS, using the command line, or using text editors like VIM. So far, we have been able to complete setting up the environment for simulating the HammerBlade and have successfully run a program. Currently, we are working on learning the CUDA-Lite syntax and programming the transpose of a matrix. At the same time, we are studying its architecture to gain a better understanding of how it is programmed. The work unit of HammerBlade CUDA-Lite is the Tile Group. It is difficult to understand how to program the HammerBlade without having a prior understanding of how its architecture functions. This quarter will be spent learning how to build basic programs in CUDA-Lite and understanding the architecture of the HammerBlade. In the future, we’d like to study more of the properties of the Hammerblade, such as its energy efficiency.
Emily Romero, Kashyap Panda, Nicole Garcia
Enhancing performance and making technology cost effective is a critical objective that many aim to achieve. The Raspberry Pi is an accessable, lost cost computer but lacks signifcant computaional power. Our team's goal is to optimize computation utilizing the Raspberry Pi Quad Processing Unit (QPU). We will be comparing the cost and energy efficiency of the Raspberry Pi’s QPU by developing optimized BLAS libraries. Overall, our main objective is to create embedded systems that are price accessible and that have high performance rates.
With embedded applications running services, such as neural networks, embed boards such as Nvidia’s Jetson lineup are able to offer a good performance-per-watt metric. With the need for low power consumption, being able to scale the frequency to best match the current load should be able to offer a lower power consumption. We can measure the impact that GPU Dynamic Frequency and Voltage Scaling (DVFS) can have on power consumption by scaling resources based on the current system load. Being able to run applications on the minimum power while maintaining a Quality of Service requirement can lead to extended service when power is supplied by a finite source. The goal of this research is to create a DVFS Service that can run alongside applications and scale the CPU, GPU, and other accelerators to an appropriate level based on the current system usage. This would allow for an overall decrease in power consumption, leading to longer operation.
The following schedule is tentative and is subject to change.
|1||March 29||Daniel Wong||Welcome to Research!|
|2||April 5||Marcus Chow||Introduction to Parallel Architectures||Parallel_Computer_Architectures.pdf|
|3||April 16||Emerson Jacoboson||Dynamic Voltage and Frequency Scaling on Embedded Systems||DVFS.pdf|
|4||April 23||Kiran Ranganath||Multi-Accelerator Systems||MultiAccelerator_Systems.pdf|
|5||April 30||AmirAli Abdolrashidi||Data Dependency in GPUs||GPU_Data_Dependecy.pdf|
|6||May 7||Ana Cardenas Beltran||Hammerblade Manycore||Hammerblade_Manycore.pdf|
|7||May 14||Emily,Kashyap,Nikole||BLAS for Raspberry Pi QPU||Raspberry_Pi_QPU.pdf|
|8||May 21||Bobby, Mika||GPU Tasking|
|9||May 28||Vahagn Tovmasian||CU Scaling|