mirage-project/mirage
Nov 2025 – Jun 2026
5 Months active
Languages Used
C++CUDAPythonC
Technical Skills
CUDAGPU ProgrammingParallel ComputingPerformance OptimizationMachine LearningPython Development
Zephyr Zhao
PROFILE
Zephyr Zhao
Contributed to the mirage-project/mirage repository by developing advanced multi-GPU data processing features, including a robust identity layer for intermediate result capture and optimized NVSHMEM-based communication paths. Leveraged C++, CUDA, and Python to implement tile-based Allreduce, FP8 group GEMM, and tensor view support, focusing on performance, memory efficiency, and correctness in deep learning workflows. Enhanced test infrastructure with unit tests, profiling improvements, and lazy CUDA driver initialization to streamline validation and startup. Refactored code for maintainability, upgraded to C++20, and addressed bugs in model loading and task execution, enabling scalable, reliable, and efficient multi-GPU machine learning pipelines.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
11Total
Bugs
2
Commits
11
Features
9
Lines of code
14,436
Activity Months5
Your Network
24 peopleShared Repositories
24
Work History
June 2026
3 Commits • 3 Features
Jun 1, 2026June 2026 — Mirage project monthly summary. Delivered foundational tensor view support, enhanced test-mode capabilities with profiling improvements, and streamlined NVSHMEM integration. The work emphasized memory efficiency, correctness, and developer productivity across tensor operations and testing workflows.
3 Commits • 3 Features
Jun 1, 2026June 2026 — Mirage project monthly summary. Delivered foundational tensor view support, enhanced test-mode capabilities with profiling improvements, and streamlined NVSHMEM integration. The work emphasized memory efficiency, correctness, and developer productivity across tensor operations and testing workflows.
June 2026
April 2026
3 Commits • 2 Features
Apr 1, 2026April 2026 monthly summary for mirage-project/mirage: Delivered high-impact FP8 capabilities and reinforced validation infrastructure, driving performance and reliability for DL workloads.
April 2026
3 Commits • 2 Features
Apr 1, 2026April 2026 monthly summary for mirage-project/mirage: Delivered high-impact FP8 capabilities and reinforced validation infrastructure, driving performance and reliability for DL workloads.
February 2026
2 Commits • 2 Features
Feb 1, 2026February 2026 Mirage project monthly summary (2026-02). Key focus was delivering performance-oriented multi-GPU communication and startup improvements. Highlights include a tile-based Allreduce path using NVSHMEM with unit tests and code modernization to C++20, plus lazy CUDA driver initialization to reduce startup overhead. These changes improved scalability and maintainability, with correctness validated on multi-GPU configurations (e.g., Blackwell). Commit references include: ae910e7dbb5cfa0cd49c0096e857fde7d5a18fc2 for the NVSHMEM Allreduce work and 95cc6106402e07e69000109fb564ccb59de77079 for the lazy CUDA driver initialization.
2 Commits • 2 Features
Feb 1, 2026February 2026 Mirage project monthly summary (2026-02). Key focus was delivering performance-oriented multi-GPU communication and startup improvements. Highlights include a tile-based Allreduce path using NVSHMEM with unit tests and code modernization to C++20, plus lazy CUDA driver initialization to reduce startup overhead. These changes improved scalability and maintainability, with correctness validated on multi-GPU configurations (e.g., Blackwell). Commit references include: ae910e7dbb5cfa0cd49c0096e857fde7d5a18fc2 for the NVSHMEM Allreduce work and 95cc6106402e07e69000109fb564ccb59de77079 for the lazy CUDA driver initialization.
February 2026
January 2026
2 Commits • 1 Features
Jan 1, 2026Month: 2026-01 — Mirage project monthly summary focused on key accomplishments in hopper task correctness and multi-GPU NVSHMEM performance optimization. Highlights include delivering a critical bug fix for hopper task output and implementing a substantial Allreduce performance improvement for NVSHMEM-enabled multi-GPU environments. Key features delivered: - Allreduce Performance Optimization for Multi-GPU NVSHMEM: refactor to decouple grid dimensions, optimize NVSHMEM copy operations, and introduce separate tasks for allgather and reduction to boost efficiency and scalability. Major bugs fixed: - Hopper Task Correctness and Model Loading in Mirage: fix hopper output correctness, correct model loading, task registration parameters, and handling of residuals in linear operations to improve correctness. Overall impact and accomplishments: - Increased reliability and correctness of hopper task execution, enabling more accurate results and smoother model workflows. - Improved multi-GPU throughput and scalability through targeted NVSHMEM task refactoring and copy optimizations, contributing to faster end-to-end compute pipelines. - Clear evidence of engineering discipline in performance optimization, code refactoring, and correctness verification across GPU-parallel tasks. Technologies/skills demonstrated: - NVSHMEM, multi-GPU parallelism, grid dimension decoupling, NVSHMEM copy optimization, task decomposition (allgather/reduction), performance tuning, and robustness fixes. - Strong collaboration markers via commit-driven changes and verification of correctness.
January 2026
2 Commits • 1 Features
Jan 1, 2026Month: 2026-01 — Mirage project monthly summary focused on key accomplishments in hopper task correctness and multi-GPU NVSHMEM performance optimization. Highlights include delivering a critical bug fix for hopper task output and implementing a substantial Allreduce performance improvement for NVSHMEM-enabled multi-GPU environments. Key features delivered: - Allreduce Performance Optimization for Multi-GPU NVSHMEM: refactor to decouple grid dimensions, optimize NVSHMEM copy operations, and introduce separate tasks for allgather and reduction to boost efficiency and scalability. Major bugs fixed: - Hopper Task Correctness and Model Loading in Mirage: fix hopper output correctness, correct model loading, task registration parameters, and handling of residuals in linear operations to improve correctness. Overall impact and accomplishments: - Increased reliability and correctness of hopper task execution, enabling more accurate results and smoother model workflows. - Improved multi-GPU throughput and scalability through targeted NVSHMEM task refactoring and copy optimizations, contributing to faster end-to-end compute pipelines. - Clear evidence of engineering discipline in performance optimization, code refactoring, and correctness verification across GPU-parallel tasks. Technologies/skills demonstrated: - NVSHMEM, multi-GPU parallelism, grid dimension decoupling, NVSHMEM copy optimization, task decomposition (allgather/reduction), performance tuning, and robustness fixes. - Strong collaboration markers via commit-driven changes and verification of correctness.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 highlights for mirage-project/mirage: Delivered a robust multi-GPU data processing reliability and identity layer enhancements, enabling reliable capture of intermediate results and improved data synchronization across GPUs. Implemented correctness fixes and performance optimizations to support maximum sequence lengths, including switching accumulation to float and stabilizing the request finish condition. Strengthened data transfer performance with loop-based strided transfers using NVSHMEM and ensured proper NVSHMEM event handling. Improved maintainability by separating CUDA source files by rank and adding comprehensive checks for CUDA functions and NVSHMEM allocations. Validated robustness across 4- and 8-GPU configurations, contributing to higher throughput and reliability in large-scale deployments.
1 Commits • 1 Features
Nov 1, 2025November 2025 highlights for mirage-project/mirage: Delivered a robust multi-GPU data processing reliability and identity layer enhancements, enabling reliable capture of intermediate results and improved data synchronization across GPUs. Implemented correctness fixes and performance optimizations to support maximum sequence lengths, including switching accumulation to float and stabilizing the request finish condition. Strengthened data transfer performance with loop-based strided transfers using NVSHMEM and ensured proper NVSHMEM event handling. Improved maintainability by separating CUDA source files by rank and adding comprehensive checks for CUDA functions and NVSHMEM allocations. Validated robustness across 4- and 8-GPU configurations, contributing to higher throughput and reliability in large-scale deployments.
November 2025
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Quality Metrics
Correctness90.8%
Maintainability81.8%
Architecture83.6%
Performance85.4%
AI Usage40.0%
Skills & Technologies
Programming Languages
CC++CUDAPython
Technical Skills
C++C++ DevelopmentC++ developmentCUDACUDA DevelopmentDeep LearningGPU ProgrammingMachine LearningMulti-GPU ProgrammingParallel ComputingPerformance OptimizationProfilingPythonPython DevelopmentPython Scripting
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