KEY RESPONSIBILITIES
Scalability Testing: Evaluate the scalability of GPU clusters by conducting thorough testing under various workloads, ensuring optimal performance across different cluster sizes, configurations, and networking technologies (RoCE)
Benchmarking and Analysis: Develop and execute comprehensive benchmarking strategies to assess baseline performance, analyze bottlenecks, and identify areas for improvement within GPU cluster environments.
Cluster Network & Performance Optimization: Collaborate with hardware and software teams to enhance the overall performance of GPU clusters, focusing on aspects such as RDMA throughput, RoCE v2 network congestion, latency, and collective communications.
Performance Profiling: Utilize profiling tools and methodologies to analyze and identify performance bottlenecks, providing actionable insights for improvement.
Performance Tuning: Implement optimization strategies, including but not limited to protocol enhancements, load balancing techniques, and parallel processing optimizations.
Documentation: Create detailed documentation of performance analysis, tuning efforts, and outcomes, providing clear and concise reports for internal teams and stakeholders.
Collaboration: Work closely with cross-functional teams, including hardware engineers, software developers, and system architects, to integrate performance improvements into the GPU cluster architecture.
Continuous Learning: Stay current with the latest developments in GPU architectures, parallel processing, and emerging technologies to drive continuous improvement in GPU cluster performance.
PREFERRED EXPERIENCE
Proven experience in optimizing the performance of GPU clusters.
RDMA network configuration, troubleshooting and performance tuning.
Strong understanding of GPU architectures, parallel computing concepts, and network protocols.
Proficiency in scripting languages (e.g., Python, Bash) for automation and performance analysis.
Experience with system level performance analysis tools and methodologies for GPU clusters.
Analytical mindset with excellent problem-solving and debug skills.
Familiarity with cluster management tools and systems.
Excellent communication and collaboration skills for effective teamwork.
Linux kernel networking expertise
Machine learning and/or HPC system design
ACADEMIC CREDENTIALS
Bachelors or Master’s degree in computer science or electrical engineering
LOCATION
Austin TX, Seattle WA, Santa Clara CA or Secaucus NJ
This role is not eligible for visa sponsorship.
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
THE ROLE
We are seeking a highly motivated and skilled Principal AI Cluster Performance Validation Engineer to join our dynamic team. In this role, you will be at the forefront of optimizing and achieving peak performance for GPU clusters. The focus of this role is the RDMA networks used in AI Clusters, understanding data flows between GPU, NIC and cluster network. The ideal candidate will have a strong background in GPU architectures, parallel clustered computing, and hands-on experience in system level performance tuning and debug methodologies.
THE PERSON
The team fosters and encourages continuous technical innovation to showcase successes as well as facilitate continuous career development. A seasoned professional who enjoys hands-on problem-solving. In this role, you’ll shape long-term strategy, drive feature enablement and jump in to tackle challenges head-on. You’ll have a direct impact on performance, automation, and validation, while staying ahead of industry trends to provide strategic insights to senior management. The person should be experienced in debugging complex HW/FW and clustered configurations.
KEY RESPONSIBILITIES
Scalability Testing: Evaluate the scalability of GPU clusters by conducting thorough testing under various workloads, ensuring optimal performance across different cluster sizes, configurations, and networking technologies (RoCE)
Benchmarking and Analysis: Develop and execute comprehensive benchmarking strategies to assess baseline performance, analyze bottlenecks, and identify areas for improvement within GPU cluster environments.
Cluster Network & Performance Optimization: Collaborate with hardware and software teams to enhance the overall performance of GPU clusters, focusing on aspects such as RDMA throughput, RoCE v2 network congestion, latency, and collective communications.
Performance Profiling: Utilize profiling tools and methodologies to analyze and identify performance bottlenecks, providing actionable insights for improvement.
Performance Tuning: Implement optimization strategies, including but not limited to protocol enhancements, load balancing techniques, and parallel processing optimizations.
Documentation: Create detailed documentation of performance analysis, tuning efforts, and outcomes, providing clear and concise reports for internal teams and stakeholders.
Collaboration: Work closely with cross-functional teams, including hardware engineers, software developers, and system architects, to integrate performance improvements into the GPU cluster architecture.
Continuous Learning: Stay current with the latest developments in GPU architectures, parallel processing, and emerging technologies to drive continuous improvement in GPU cluster performance.
PREFERRED EXPERIENCE
Proven experience in optimizing the performance of GPU clusters.
RDMA network configuration, troubleshooting and performance tuning.
Strong understanding of GPU architectures, parallel computing concepts, and network protocols.
Proficiency in scripting languages (e.g., Python, Bash) for automation and performance analysis.
Experience with system level performance analysis tools and methodologies for GPU clusters.
Analytical mindset with excellent problem-solving and debug skills.
Familiarity with cluster management tools and systems.
Excellent communication and collaboration skills for effective teamwork.
Linux kernel networking expertise
Machine learning and/or HPC system design
ACADEMIC CREDENTIALS
Bachelors or Master’s degree in computer science or electrical engineering
LOCATION
Austin TX, Seattle WA, Santa Clara CA or Secaucus NJ
This role is not eligible for visa sponsorship.
Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here.
This posting is for an existing vacancy.
