pytorch/FBGEMM
Jun 2025 – Jul 2025
2 Months active
Languages Used
C++CUDA
Technical Skills
C++CUDAGPU ProgrammingPerformance Optimization
Haoqiang Guo
PROFILE
Haoqiang Guo
Over a two-month period, contributed to the pytorch/FBGEMM repository by developing and optimizing AMD GPU kernels for reordering batched ad indices. Focused on performance optimization, the work introduced a vectorized kernel supporting Long and float data types, with a broadcast_indices option to address specialized use cases. Leveraging C++, CUDA, and GPU programming expertise, implemented AMD-specific thread block sizing and conditional logic to maximize compute utilization for large-scale, data-intensive workloads. These enhancements improved throughput and reduced latency for high product length and ad count scenarios, strengthening cross-architecture support and laying the groundwork for broader hardware-specific performance improvements in FBGEMM.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
150
Activity Months2
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2993 peopleSame Organization
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Shared Repositories
195
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 Monthly Summary (pytorch/FBGEMM) This month focused on AMD-optimized kernel enhancements to improve performance for reordering batched ad indices, targeting workloads with large product lengths and high ad counts. The work emphasizes compute utilization on AMD GPUs through vectorized kernel pathways and data-type aware configurations, laying groundwork for broader hardware-specific performance gains in FB-GEMM.
1 Commits • 1 Features
Jul 1, 2025July 2025 Monthly Summary (pytorch/FBGEMM) This month focused on AMD-optimized kernel enhancements to improve performance for reordering batched ad indices, targeting workloads with large product lengths and high ad counts. The work emphasizes compute utilization on AMD GPUs through vectorized kernel pathways and data-type aware configurations, laying groundwork for broader hardware-specific performance gains in FB-GEMM.
July 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Focused AMD optimization within FBGEMM. Delivered a vectorized AMD-specific kernel reorder_batched_ad_indices_kernel_vec for reordering batched ad indices, with support for Long and float data types and a broadcast_indices option. This work is recorded under commit 8ba51842cb2a3c143cd93a0ee8ea54a69893c159 in pytorch/FBGEMM. No major bugs reported for this period; the feature enhances throughput for data-heavy workloads on AMD hardware and strengthens cross-arch performance parity.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Focused AMD optimization within FBGEMM. Delivered a vectorized AMD-specific kernel reorder_batched_ad_indices_kernel_vec for reordering batched ad indices, with support for Long and float data types and a broadcast_indices option. This work is recorded under commit 8ba51842cb2a3c143cd93a0ee8ea54a69893c159 in pytorch/FBGEMM. No major bugs reported for this period; the feature enhances throughput for data-heavy workloads on AMD hardware and strengthens cross-arch performance parity.
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CUDA
Technical Skills
C++CUDAGPU ProgrammingPerformance Optimization
Repositories Contributed To
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