Key skills
HPC experienceParallel programmingPerformance benchmarkingC/C++ programmingPython scriptingComputer system architecturePassion to learnDebugging skillscontainerscloud toolsAdaptabilityCollaborationFlexibilityCommunication skillsPythonC++CArtificial IntelligenceDeep LearningPyTorchTensorFlowKubernetesDockerAnsibleCommunication
About this role
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The role involves conducting performance characterization and analysis on large multi-GPU and multi-node clusters, evaluating proof-of-concepts, and collaborating with a dynamic team to improve communication libraries.
Responsibilities:
- Conduct in-depth performance characterization and analysis on large multi-GPU and multi-node clusters
- Study the interaction of our libraries with all HW (GPU, CPU, Networking) and SW components in the stack
- Evaluate proof-of-concepts, conduct trade-off analysis when multiple solutions are available
- Triage and root-cause performance issues reported by our customers
- Collect a lot of performance data; build tools and infrastructure to visualize and analyze the information
- Collaborate with a very dynamic team across multiple time zones
Requirements:
- M.S. (or equivalent experience) or PhD in Computer Science, or related field with relevant performance engineering and HPC experience
- 3+ yrs of experience with parallel programming and at least one communication runtime (MPI, NCCL, UCX, NVSHMEM)
- Experience conducting performance benchmarking and triage on large scale HPC clusters
- Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)
- Implement micro-benchmarks in C/C++, read and modify the code base when required
- Ability to debug performance issues across the entire HW/SW stack. Proficient in a scripting language, preferably Python
- Familiar with containers, cloud provisioning and scheduling tools (Kubernetes, SLURM, Ansible, Docker)
- Adaptability and passion to learn new areas and tools. Flexibility to work and communicate effectively across different teams and timezones
- Practical experience with Infiniband/Ethernet networks in areas like RDMA, topologies, congestion control
- Experience debugging network issues in large scale deployments
- Familiarity with CUDA programming and/or GPUs
- Experience with Deep Learning Frameworks such PyTorch, TensorFlow