Big Data Analytics Benchmarks for Ara

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

• Chi Zhang: [1]
• Matteo Perotti: [2]

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