Goal and Deliverable for Week One, Week Two

The target for the first two weeks is as follows.

Time: May 22 - June 2, 2023

Weeks 1-2 Goal - Onboarding

Goal: Familiarization with HPCC Systems GNN Bundle, Tensorflow, reading research papers, and background knowledge.

In order to achieve this goal, I will

Learn HPCC Environment.
Learn the implementation of the GNN Bundle.
Build an image classifier network using GNN Bundle (Mnist Dataset, Imagenet Dataset).
Replicated some of the work previously done in the HPCC System using GNN Bundle [1, 2].

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Enhancing Performance of Distributed Neural Network with GNN Bundle - Project Poster and Videos

Poster contents Background Deep learning implementation demands are rising in various fields, including manufacturing, medicine, art, history, etc. The high demand for powerful graphics cards has led to shortages. Generalized Neural Network (GNN) being one of the choices for distributed neural network training; we want to improve it to have better control over machine utilization and optimize its performance. Within GNN, during backpropagation, the time required for each iteration is equal to the time for the weakest node due to the aggregation of loss required at the end of every iteration Research Questions Can we improve resource usage reduce training time required for deep neural network training using the more powerful machine? Method Adopted the notion of training with a restricted node count Enhanced the Fit function to accommodate nodeNumber parameter. Extracted data aligned with the chosen node count. Conducted training and weight synchronization on chosen nodes. Redistributed...

Week 9: Weekly work summary

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Week 10: Weekly work summary

Project: Distributed Neural Network Training and Prediction Mentors: Roger Dev, Lili Xu Week 10: Weekly work summary (July 24-28) During this week, I participated in a short demo on utilizing ecl watch for code analysis, specifically focusing on analyzing time for each segment of the code. Gordon Smith provided us with a helpful walkthrough on effectively using Filters and Cross Tab from Metrics page to gather valuable analytics information. Additionally, he demonstrated the ECL tab, offering a brief overview of the time associated with each code block and its color code. I learned that we have the capability to update graph labels through the Options -> Graph tab. This session was highly informative and enhanced my understanding of the analytics available on ECL Watch. Following the demonstration, I devoted my time to analyzing the performance of the single node and nNodeFit Functions. Notably, I observed that FitBatch did not exhibit any time for a single node. To address this, I ...

Summer 23 with HPCC Systems

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