Big Data Analytics Benchmarks for Ara
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Introduction
Ara big data analytics Ara lack these benchmarks goal:
Tasks
- Familiarize yourself with vector processor Ara
- Try to run Ara RTL simulation
- Executing existing benchmarks
- Understand how vector processor works and the chaining techneque
- Familiarize yourself with a bunch of popular big data analytics worksloads, including:
- Naive Bayes
- SVM
- K-means clustering
- Breadth-first search
- Depth-first search
- Multilayer perceptron,
- Graph neural network
- Coding for big data analytics benchmarks for Ara, while think about:
- How to vectorize these workloads
- How to schedule memory access and computation to make best advantage of vector chaining and reach to high function unit utilization
- Evaluating big data analytics benchmarks
- Run you benchmarks on Ara and count performance metrics, function unit utilization, bandwidth, bus utilization, etc.
- Make roofline model, while varing data set size and Ara lane counts
- Write a report and prepare a presentation.
- Possible BONUS goals.
Requirements
- Strong interest and basic knowledge in computer architecture and operating systems, both on the HW and SW sides
- Experience with SystemVerilog HDL, such as taught in VLSI I
- Knowledge of bare-metal C and assembly programming
- Bonus: being familiar with vector processors, RISC-V RVV
Character
- 25% Literature / Architecture review
- 50% Bare-metal C and Assembly programming
- 25% Performance evaluation
Project Supervisors
References
[1] Ara: https://arxiv.org/pdf/1906.00478.pdf
[2] Ara source code: https://github.com/pulp-platform/ara
[3] Cray-Processor: http://www.edwardbosworth.com/My5155_Slides/Chapter13/Cray_Supercomputers.htm
[4] RVV: https://github.com/riscv/riscv-v-spec/releases/tag/v1.0