DL and HPC applications have a huge compute demands and already run at scales of up to tens of thousands of GPUs. GPUs are connected with high-speed interconnects (e.g. NVLink, PCIe) within a node and with high-speed networking (e.g. InfiniBand, Ethernet) across nodes. Efficient and fast communication between GPUs directly impacts end-to-end application performance. This impact continues to grow with the increasing scale of next generation systems. This is an outstanding opportunity to advance the state-of-the-art, break performance barriers, and deliver platforms the world has never seen before. Are you ready to build the new and innovative technologies that will help realize our vision?
What you will be doing:
Investigate opportunities to improve communication performance by identifying bottlenecks in today's systems.
Design and implement new communication technologies to accelerate AI and HPC workloads.
Explore innovative solutions in HW and SW for our next generation platforms as part of co-design efforts involving GPU, Networking, and SW architects.
Build proofs-of-concept, conduct experiments, and perform quantitive modeling to evaluate and drive new innovations.
Use simulation to explore performance of large GPU clusters (think scales of 100s of 1000s of GPUs).
What we need to see:
M.S./Ph.D. degree in CS/CE or equivalent experience.
5+ years of relevant experience.
Excellent C/C++ programming and debugging skills.
Experience with parallel programming models (MPI, SHMEM) and at least one communication runtime (MPI, NCCL, NVSHMEM, OpenSHMEM, UCX, UCC).
Deep understanding of operating systems, computer and system architecture.
Solid in fundamentals of network architecture, topology, algorithms, and communication scaling relevant to AI and HPC workloads.
Strong experience with Linux.
Ability and flexibility to work and communicate effectively in a multi-national, multi-time-zone corporate environment.
Ways to stand out from the crowd:
Expertise in related technology and passion for what you do. Experience with CUDA programming and our GPUs. Knowledge of high-performance networks like InfiniBand, RoCE, NVLink, etc.
Experience with Deep Learning Frameworks such PyTorch, TensorFlow, etc. Knowledge of deep learning parallelisms and mapping to the communication subsystem. Experience with HPC applications.
Strong collaborative and interpersonal skills and a proven track record of effectively guiding and influencing within a dynamic and multi-functional environment.
