Nvidia

HPC Performance Engineer

Nvidia
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1 day ago
Remote, United Kingdom +3 moreSenior

Responsibilities

  • Analyze the effects of compilers and compiler optimizations on HPC application performance.
  • Evaluate compiler and runtime improvements for HPC applications on CPU and CPU/GPU platforms.
  • Communicate compiler capability limitations to application engineering teams.
  • Port HPC applications to different platforms and programming models.
  • Evaluate the performance impact of NVHPC components, device drivers, and system configurations.
  • Collaborate with compiler development and application engineering teams to deliver performance improvements.

Requirements

  • BS/MS or equivalent experience in computer science or a related engineering field.
  • 5+ years of programming experience.
  • Strong understanding of Fortran, C, C++, and programming techniques for parallel architectures.
  • Experience with OpenACC, OpenMP, MPI, CUDA, and Standard Language Parallelism.
  • Strong performance analysis and tuning skills and broad knowledge of parallel application development tools and runtime environments.
  • Strong mathematical fundamentals, including linear algebra and numerical methods.
  • Strong understanding of performance considerations, tradeoffs, and impact.
  • Effective interpersonal, problem-solving, time-management, prioritization, written-communication, and verbal-communication skills.
  • Deep understanding of machine architectures and micro-architectures is advantageous.
  • Experience with debugging, porting, and assembly language programming is advantageous.

Benefits

  • NVIDIA offers competitive salaries and a comprehensive benefits package.
  • NVIDIA provides a diverse and supportive work environment and is an equal opportunity employer.

Tech Stack

CC++Fortran

Categories

Nvidia

About Nvidia

10,000+ employees

Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.

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