NVIDIA
Technology
Architect-GPUPerformance
Neural analysis suggests this role is
optimal for Mid candidates.
“Architect - GPU Performance at NVIDIA. Skills: GPU Performance, System-on-Chip (SoC) architecture, performance analysis, C/C++, Python. System level performance analysis/ bottleneck analysis of complex, high performance GPUs and System-on-Chips (SoCs). Work on hardware models of different levels of abstraction, including performance models, RTL test benches, emulators and silicon to analyze performance and find performance bottlenecks in the system”
What You'll Achieve.
make our next generation visual computing, automotive, GPU, HPC systems better; help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field
Industry & Context.
bottleneck analysis; find performance bottlenecks in the system; explore architecture trade-offs; debugging; analysis (including data and statistical analysis)
What They're Looking For.
Must Have
3+ years of experience with exposure to performance analysis and complex system on chip and/or GPU architectures, understanding of System-on-Chip (SoC) architecture, graphics pipeline, memory subsystem architecture and Network-on-Chip (NoC)/Interconnect architecture, Expert hands on competence in programming (C/C++) and scripting (Perl/Python)
Nice to Have
PhD is a plus, Exposure to Verilog/System Verilog, SystemC/TLM is a plus, Hands on experience developing performance simulators, cycle accurate/approximate models for pre-silicon performance analysis is a plus
What You'll Do.
System level performance analysis/ bottleneck analysis of complex
high performance GPUs and System-on-Chips (SoCs)
Work on hardware models of different levels of abstraction
including performance models
emulators and silicon to analyze performance and find performance bottlenecks in the system
Understand key performance use-cases of the product
Develop workloads and test suits targeting graphics
compute vision applications running on these products
Work closely with the architecture and design teams to explore architecture trade-offs related to system performance
and power consumption
Develop required infrastructure including performance models
performance analysis and visualization tools
Drive methodologies for improving turnaround time
finding representative data-sets and enabling performance analysis early in the product development cycle
How You'll Work.
Team & Collaboration
Work closely with the architecture and design teams
Full Job Description
NVIDIA has continuously reinvented itself. Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming worldwide, which calls for highly scalable and massively parallel computation horsepower that NVIDIA GPUs excel. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can address, and that matter to the world. This is our life’s work , to amplify human creativity and intelligence. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join our diverse team and see how you can make a lasting impact on the world. As part of this team, you would be working on projects that will help make our next generation visual computing, automotive, GPU, HPC systems better. You will get to work on high performance CPU and Memory sub-systems, Next-Gen GPUs , NOC based Interconnect Fabric etc. Make the choice to join us today. **What you 'll be doing:** * System level performance analysis/ bottleneck analysis of complex, high performance GPUs and System-on-Chips (SoCs). * Work on hardware models of different levels of abstraction, including performance models, RTL test benches ,emulators and silicon to analyze performance and find performance bottlenecks in the system. * Understand key performance use-cases of the product. Develop workloads and test suits targeting graphics, machine learning, automotive, video, compute vision applications running on these products. * Work closely with the architecture and design teams to explore architecture trade-offs related to system performance, area, and power consumption. * Develop required infrastructure including performance models, testbench components, performance analysis and visualization tools. * Drive methodologies for improving turnaround time, finding represe
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