N
Not Specified Permanent

Austin, Texas · USA job

Senior CPU Performance Architect

Nvidia

Austin, Texas

Job description

Overview

In this role you will advance CPU performance for AI/DL, HPC, CSP, gaming, and autonomous vehicle workloads. You will bring up workloads, analyze performance on silicon and simulators, and collaborate with CPU and interconnect architects to guide next-generation designs. You'll benchmark NVIDIA CPUs against competitors and identify software or hardware improvements. This is a hands-on, impact-driven position at the forefront of accelerated computing.

Compensation / Benefits
  • equity
  • benefits
  • career growth
  • competitive salary
  • global team
  • diverse and inclusive environment
Responsibilities
  • Conduct workload bring-up and performance analysis on silicon and full-system simulators
  • Study diverse workloads across AI/DL, CSP, HPC, and autonomous vehicles to identify critical behaviors
  • Analyze performance scaling bottlenecks on multi-core and multi-socket CPU and CPU/GPU systems
  • Collaborate with CPU and interconnect architects to influence future designs based on findings
  • Benchmark NVIDIA CPU offerings against competition and propose improvements
  • Develop performance test plans and drive benchmarking for CPU and I/O
  • Explore real-world use-cases and distill them into directed test cases
Key requirements
  • BS/MS in Electrical Engineering, Computer Science, Computer Engineering, or equivalent experience
  • 12+ years of relevant experience
  • Experience with CPU workloads and performance analysis
  • Knowledge of performance test development and benchmarking for CPU and I/O
  • Deep knowledge of CPU microarchitecture and system architecture
  • Experience with ARM ISA preferred but not required
  • CPU workloads and performance analysis
  • Performance test development and benchmarking for CPU and I/O
  • Deep knowledge of CPU microarchitecture and system architecture
  • Experience with the ARM ISA (preferred)
  • GPU driver experience (optional)
  • Knowledge of GPU-accelerated workloads and modeling performance of accelerated workloads

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