
Distributed AI Workloads Optimizer
4 days ago
About the Project
We are working on a project to develop collective communications software library and optimize distributed AI/ML workloads' performance on our in-house silicon AI accelerators. In this role, you will be responsible for implementing simulation models for our Accelerator ASICs, develop and analyze various scenarios to evaluate data center performance and identify potential improvements.
Key Responsibilities- Implement simulation models for our Accelerator ASICs, develop and analyze various scenarios to evaluate data center performance and identify potential improvements.
- Contribute to the development of our developer infrastructure, including simulation and HW emulation platforms, to enable performance measurement and optimization for our in-house accelerator programs.
- Understand and contribute to the collective communications library, intended to be deployed on our AI/ML superclusters.
- Support networking and compute hardware acceleration techniques to improve ML inference and training model performance.
- Perform architectural analysis to ensure system designs meet performance, scalability, and reliability requirements.
- Collaborate with architects and engineers to integrate simulation results into system design processes.
- Use instruction set simulators to define performant firmware for our training/inference accelerators.
- Collaborate with hardware and firmware teams to ensure accurate modeling and simulation of accelerator functionalities.
- Analyze simulation results to guide firmware development and optimization efforts.
- Bachelor or higher in Computer Science, Computer Engineering, or any other relevant technical field.
- 5+ years experience in developing C++ codebase.
- 5+ years experience in developing Python codebase.
- Understanding of performance and benchmarking measurement and optimization on collective communications and distributed at-scale model training.
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