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Architect - GPU Performance Analysis

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NVIDIA

Sep 12

  • Job
    Full-time
    Mid Level
  • Software Engineering
    Engineering
  • Bangalore
  • Quick Apply

AI generated summary

  • You need a relevant degree, 3+ years in performance analysis of SoC/GPU, strong SoC and memory architecture knowledge, proficient in C/C++ and Python, and experience with performance simulation.
  • You will analyze GPU performance, develop workloads, optimize architectures, create performance models, and enhance analysis methodologies to identify bottlenecks during development.

Requirements

  • What we need to see:
  • BE/BTech, or MS/MTech in relevant area, PhD is a plus, or equivalent experience.
  • 3+ years of experience with exposure to performance analysis and complex system on chip and/or GPU architectures.
  • Strong 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). Exposure to Verilog/System Verilog, SystemC/TLM is a strong plus.
  • Strong debugging and analysis (including data and statistical analysis) skills, including use for RTL dumps to debug failures.
  • Hands on experience developing performance simulators, cycle accurate/approximate models for pre-silicon performance analysis is a strong plus.

Responsibilities

  • 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 representative data-sets and enabling performance analysis early in the product development cycle.

FAQs

What is the focus of the Architect - GPU Performance Analysis role at NVIDIA?

The role focuses on system level performance analysis, bottleneck analysis of complex high-performance GPUs and System-on-Chips (SoCs), as well as developing performance models and test suites for various applications.

What qualifications are required for this position?

The position requires a BE/BTech, or MS/MTech in a relevant area; a PhD is a plus. Additionally, 3+ years of experience with performance analysis and complex SoC or GPU architectures is needed.

What programming skills are necessary for this role?

Strong competence in programming (C/C++) and scripting (Perl/Python) is required. Exposure to Verilog/System Verilog, SystemC/TLM is considered a strong plus.

What types of projects will I be involved in?

You will be involved in projects related to high-performance CPU and Memory sub-systems, next-generation GPUs, and interconnect fabric for visual computing and AI applications.

Is hands-on experience required for performance simulators?

Yes, hands-on experience in developing performance simulators and cycle-accurate/approximate models for pre-silicon performance analysis is strongly preferred.

Will I collaborate with other teams in this role?

Yes, you will work closely with architecture and design teams to explore architecture trade-offs related to system performance, area, and power consumption.

What methodologies will I be expected to drive in this position?

You will be expected to drive methodologies aimed at improving turnaround time, finding representative datasets, and enabling performance analysis early in the product development cycle.

Is experience in debugging and data analysis important for this role?

Yes, strong debugging and analysis skills, including the ability to perform data and statistical analysis, are essential for this position.

What industry sectors will my work contribute to?

Your work will contribute to various sectors, including graphics, machine learning, automotive, video, and computer vision applications.

Does NVIDIA value diversity in hiring?

Yes, NVIDIA is an equal opportunity employer and values diversity at their company, ensuring that they do not discriminate on various bases including race, gender, age, and more.

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NVIDIA

Manufacturing & Electronics
Industry
10,001+
Employees
1993
Founded Year

Mission & Purpose

Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.