aws-samples/awsome-distributed-training
Apr 2026 – Apr 2026
1 Month active
Languages Used
CPythonShell
Technical Skills
AWSBenchmarkingDistributed SystemsGPU ProgrammingSlurm
Paulo Gallotti
PROFILE
Paulo Gallotti
Developed and delivered the NCCL Benchmark Suite and Cluster Script for the aws-samples/awsome-distributed-training repository, enabling multi-GPU ring benchmarking to measure latency and bandwidth across distributed systems. The work included implementing a Slurm batch script tailored for SageMaker HyperPod clusters with EFA networking, reorganizing benchmark code for clarity, and enhancing documentation to support reproducibility and developer onboarding. Addressed pull request feedback by refining timing accuracy and throughput naming, and improved output reporting using MPI_Reduce for precise performance measurement. Utilized C, Python, and Shell scripting, applying expertise in AWS, benchmarking, and GPU programming to improve scalability testing workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
173,083
Activity Months1
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary: Delivered the NCCL Benchmark Suite and Cluster Script for aws-samples/awsome-distributed-training, enabling a multi-GPU ring benchmark to measure latency and bandwidth across GPUs. Added a Slurm batch script for SageMaker HyperPod clusters with EFA networking, along with reorganized code and improved documentation. Addressed PR feedback to improve motivation, timing accuracy, and throughput naming. These changes improve benchmarking reproducibility, scalability testing, and developer experience.
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary: Delivered the NCCL Benchmark Suite and Cluster Script for aws-samples/awsome-distributed-training, enabling a multi-GPU ring benchmark to measure latency and bandwidth across GPUs. Added a Slurm batch script for SageMaker HyperPod clusters with EFA networking, along with reorganized code and improved documentation. Addressed PR feedback to improve motivation, timing accuracy, and throughput naming. These changes improve benchmarking reproducibility, scalability testing, and developer experience.
April 2026
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CPythonShell
Technical Skills
AWSBenchmarkingDistributed SystemsGPU ProgrammingSlurm
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline