mirage-project/mirage
Oct 2024 – Oct 2025
12 Months active
Languages Used
C++PythonCythonDoxygenMarkdownRSTTextCUDA
Technical Skills
Build ManagementCompiler DevelopmentDemo ScriptingMemory ManagementPerformance OptimizationVersion Control
Zhihao Jia
PROFILE
Zhihao Jia
Zhihao spent the past year engineering core infrastructure for the mirage-project/mirage repository, focusing on scalable multi-GPU deep learning workloads. He developed a persistent kernel framework with lifecycle management, enabling distributed GPU computing via MPI and NVSHMEM, and extended support across Ampere, Hopper, and Blackwell architectures. Using C++, CUDA, and Python, Zhihao implemented advanced features such as checkpointing, paged attention, and task graph automation, while optimizing memory management and kernel execution for performance and reliability. His work included robust testing, profiling, and documentation updates, resulting in a maintainable, high-performance backend that accelerates model deployment and supports production-grade AI workloads.
Overall Statistics
Feature vs Bugs
76%Features
Repository Contributions
122Total
Bugs
16
Commits
122
Features
50
Lines of code
44,589
Activity Months12
Your Network
22 people
Work History
October 2025
4 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for mirage project: Key features delivered include substantial multi-GPU and cross-architecture enhancements, while maintaining rigorous test coverage to ensure performance portability across modern GPUs. The work focused on performance, correctness, and scalability of the Persistent Kernel (PK) in multi-GPU environments, plus scaffolding to support multiple GPU architectures with validated tests.
4 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for mirage project: Key features delivered include substantial multi-GPU and cross-architecture enhancements, while maintaining rigorous test coverage to ensure performance portability across modern GPUs. The work focused on performance, correctness, and scalability of the Persistent Kernel (PK) in multi-GPU environments, plus scaffolding to support multiple GPU architectures with validated tests.
October 2025
September 2025
7 Commits • 5 Features
Sep 1, 20252025-09 Monthly Summary (mirage-project/mirage) Overview: Focused on delivering high-impact features for the Mirage Persistent Kernel (MPK), improving stability of the CUDA backend, enhancing observability, and updating documentation and dependencies to support ongoing performance optimization and adoption. The work emphasizes business value through faster, more predictable GPU workloads, better diagnostic tooling, and smoother onboarding for contributors and users. Key features delivered: - Mirage Persistent Kernel: Continuous batching and paged attention on Hopper GPUs with new kernels (linear operations, paged attention, RMS normalization, swapAB) and extensive Tensor Memory Accelerator (TMA) integration; updated demos and documentation. - MPK: Shuffle tensors support in MPK library, enabling more flexible tensor manipulation and updated demos, headers, and runtime implementations. - MPK: Profiling and verbose logging via compile-time flags for improved runtime diagnostics and performance analysis. - Documentation: README update providing MPK overview, benefits, and resources (Slack, Roadmap, Blog). - Maintenance: Upgraded transformers library to 4.53.0 to include bug fixes and performance improvements (dependencies only). Major bugs fixed: - MPK CUDA backend compatibility and memory fingerprinting adjustments: Enabled CUDA backend safely and conditionally disabled memory fingerprinting for large graphs to improve stability with PR #463. - MPK: Correct memory visibility with acquire-release semantics: Refactored atomic operations in the persistent kernel to ensure correct visibility and ordering between workers and schedulers. Overall impact and accomplishments: - Delivered end-to-end enhancements to MPK that boost throughput, stability, and observability, directly supporting larger, production-grade workloads on Hopper GPUs and more reliable performance diagnostics. - Reduced debugging friction and improved correctness of concurrent memory operations, increasing developer confidence and lowering risk during future optimizations. - Improved developer experience through better tensor manipulation APIs and up-to-date documentation, enabling faster iteration and onboarding. Technologies/skills demonstrated: - CUDA kernel development and GPU-specific optimizations (Hopper GPUs), TMA integration. - Memory consistency models (acquire-release semantics) and concurrent programming patterns. - Compile-time configurability for profiling and logging. - Dependency management and release hygiene (transformers upgrade); documentation and onboarding practices.
September 2025
7 Commits • 5 Features
Sep 1, 20252025-09 Monthly Summary (mirage-project/mirage) Overview: Focused on delivering high-impact features for the Mirage Persistent Kernel (MPK), improving stability of the CUDA backend, enhancing observability, and updating documentation and dependencies to support ongoing performance optimization and adoption. The work emphasizes business value through faster, more predictable GPU workloads, better diagnostic tooling, and smoother onboarding for contributors and users. Key features delivered: - Mirage Persistent Kernel: Continuous batching and paged attention on Hopper GPUs with new kernels (linear operations, paged attention, RMS normalization, swapAB) and extensive Tensor Memory Accelerator (TMA) integration; updated demos and documentation. - MPK: Shuffle tensors support in MPK library, enabling more flexible tensor manipulation and updated demos, headers, and runtime implementations. - MPK: Profiling and verbose logging via compile-time flags for improved runtime diagnostics and performance analysis. - Documentation: README update providing MPK overview, benefits, and resources (Slack, Roadmap, Blog). - Maintenance: Upgraded transformers library to 4.53.0 to include bug fixes and performance improvements (dependencies only). Major bugs fixed: - MPK CUDA backend compatibility and memory fingerprinting adjustments: Enabled CUDA backend safely and conditionally disabled memory fingerprinting for large graphs to improve stability with PR #463. - MPK: Correct memory visibility with acquire-release semantics: Refactored atomic operations in the persistent kernel to ensure correct visibility and ordering between workers and schedulers. Overall impact and accomplishments: - Delivered end-to-end enhancements to MPK that boost throughput, stability, and observability, directly supporting larger, production-grade workloads on Hopper GPUs and more reliable performance diagnostics. - Reduced debugging friction and improved correctness of concurrent memory operations, increasing developer confidence and lowering risk during future optimizations. - Improved developer experience through better tensor manipulation APIs and up-to-date documentation, enabling faster iteration and onboarding. Technologies/skills demonstrated: - CUDA kernel development and GPU-specific optimizations (Hopper GPUs), TMA integration. - Memory consistency models (acquire-release semantics) and concurrent programming patterns. - Compile-time configurability for profiling and logging. - Dependency management and release hygiene (transformers upgrade); documentation and onboarding practices.
July 2025
2 Commits • 1 Features
Jul 1, 2025Concise monthly summary for 2025-07 covering Mirage MPK work in mirage-project/mirage. Focus on delivering scalable compute with multi-GPU support, robustness for optional inputs, and kernel-level performance improvements that unlock broader workloads and improve reliability across GPU deployments.
2 Commits • 1 Features
Jul 1, 2025Concise monthly summary for 2025-07 covering Mirage MPK work in mirage-project/mirage. Focus on delivering scalable compute with multi-GPU support, robustness for optional inputs, and kernel-level performance improvements that unlock broader workloads and improve reliability across GPU deployments.
July 2025
June 2025
24 Commits • 12 Features
Jun 1, 2025June 2025 performance summary for mirage-project/mirage. Delivered scalable multi-GPU checkpointing, extended data type support, enhanced task graph capabilities with positional embeddings, and automated frontend tooling to generate graphs and kernels. Implemented Data Transfer Fusion to accelerate data movement and improved developer productivity through Python frontend and transpiler tooling. Stabilized the codebase with targeted bug fixes and documentation updates. Impact: improved scalability and reliability across multi-GPU workloads; reduced deployment friction; accelerated task graph development.
June 2025
24 Commits • 12 Features
Jun 1, 2025June 2025 performance summary for mirage-project/mirage. Delivered scalable multi-GPU checkpointing, extended data type support, enhanced task graph capabilities with positional embeddings, and automated frontend tooling to generate graphs and kernels. Implemented Data Transfer Fusion to accelerate data movement and improved developer productivity through Python frontend and transpiler tooling. Stabilized the codebase with targeted bug fixes and documentation updates. Impact: improved scalability and reliability across multi-GPU workloads; reduced deployment friction; accelerated task graph development.
May 2025
26 Commits • 11 Features
May 1, 2025May 2025 highlights: Delivered core scalability, resilience, and performance improvements for mirage-project/mirage. Key features delivered include multi-GPU support with MPI-launch, multi-queue scheduling with four schedulers per thread block, and checkpointing with refined persistent kernel synchronization. Profiling instrumentation and subevents enable deeper performance visibility, including a persistent kernel profiler and partitioned worker development. TaskDesc memory transfer optimization from host/local to shared memory reduced access latency. Additional enhancements include JSON support and the attention task implementation to broaden configuration and capability. Several stability fixes were addressed, including array initialization, task dispatcher, and compile issue fixes, plus attention bugs in the demo.
26 Commits • 11 Features
May 1, 2025May 2025 highlights: Delivered core scalability, resilience, and performance improvements for mirage-project/mirage. Key features delivered include multi-GPU support with MPI-launch, multi-queue scheduling with four schedulers per thread block, and checkpointing with refined persistent kernel synchronization. Profiling instrumentation and subevents enable deeper performance visibility, including a persistent kernel profiler and partitioned worker development. TaskDesc memory transfer optimization from host/local to shared memory reduced access latency. Additional enhancements include JSON support and the attention task implementation to broaden configuration and capability. Several stability fixes were addressed, including array initialization, task dispatcher, and compile issue fixes, plus attention bugs in the demo.
May 2025
April 2025
6 Commits • 1 Features
Apr 1, 2025April 2025 highlights for mirage-project/mirage: Delivered a major architectural upgrade with a Persistent Kernel Framework and lifecycle management enabling distributed GPU computing via MPI/NVSHMEM. Implemented core APIs (init/launch/finalize), improved interface parameters (MPI rank, scheduler counts), added ARGMAX task type, and included data_type in TensorDesc. Added Python bindings for kernel launching and a demonstration kernel. Bug fixes around input handling and function registration improved stability and reliability.
April 2025
6 Commits • 1 Features
Apr 1, 2025April 2025 highlights for mirage-project/mirage: Delivered a major architectural upgrade with a Persistent Kernel Framework and lifecycle management enabling distributed GPU computing via MPI/NVSHMEM. Implemented core APIs (init/launch/finalize), improved interface parameters (MPI rank, scheduler counts), added ARGMAX task type, and included data_type in TensorDesc. Added Python bindings for kernel launching and a demonstration kernel. Bug fixes around input handling and function registration improved stability and reliability.
March 2025
29 Commits • 9 Features
Mar 1, 2025March 2025 highlights for mirage: Delivered core graph primitives and dataset integration, enhanced checkpointing with CUDA Graph support, and fixed key numerical issues, while advancing release readiness and developer experience through code cleanup, documentation updates, and demo integrations.
29 Commits • 9 Features
Mar 1, 2025March 2025 highlights for mirage: Delivered core graph primitives and dataset integration, enhanced checkpointing with CUDA Graph support, and fixed key numerical issues, while advancing release readiness and developer experience through code cleanup, documentation updates, and demo integrations.
March 2025
February 2025
12 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for mirage project (repo: mirage-project/mirage). Focus areas involved bug fixes, model integration, and core library improvements to enhance stability, performance, and developer productivity. Delivered stabilizing fixes for Qwen2 kernel/memory management, integrated Qwen2 with optimized attention and generation workflow, and upgraded core utilities and memory management across the library.
February 2025
12 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for mirage project (repo: mirage-project/mirage). Focus areas involved bug fixes, model integration, and core library improvements to enhance stability, performance, and developer productivity. Delivered stabilizing fixes for Qwen2 kernel/memory management, integrated Qwen2 with optimized attention and generation workflow, and upgraded core utilities and memory management across the library.
January 2025
5 Commits • 2 Features
Jan 1, 2025January 2025: Delivered key robustness and documentation enhancements for the NKI Transpiler in mirage-project/mirage, focusing on reliability, maintainability, and developer experience. The main release, 0.2.3, includes fixes to input loader and output saver, improvements to tensor layout resolution, and enhanced transpilation operation handling, complemented by a Hopper architecture-related refactor. Documentation and build stability were strengthened by pinning Sphinx and updating CUDA/Triton transpiler docs and README messaging.
5 Commits • 2 Features
Jan 1, 2025January 2025: Delivered key robustness and documentation enhancements for the NKI Transpiler in mirage-project/mirage, focusing on reliability, maintainability, and developer experience. The main release, 0.2.3, includes fixes to input loader and output saver, improvements to tensor layout resolution, and enhanced transpilation operation handling, complemented by a Hopper architecture-related refactor. Documentation and build stability were strengthened by pinning Sphinx and updating CUDA/Triton transpiler docs and README messaging.
January 2025
December 2024
3 Commits • 3 Features
Dec 1, 2024Month: 2024-12 — Delivered foundational NKI Transpiler capabilities, enhanced performance profiling, and strengthened evaluation tooling for kernel graphs. Also updated contribution attribution for the Mirage project. These efforts establish end-to-end translation, improve evaluation rigor, and support clearer collaboration.
December 2024
3 Commits • 3 Features
Dec 1, 2024Month: 2024-12 — Delivered foundational NKI Transpiler capabilities, enhanced performance profiling, and strengthened evaluation tooling for kernel graphs. Also updated contribution attribution for the Mirage project. These efforts establish end-to-end translation, improve evaluation rigor, and support clearer collaboration.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 - Mirage transpiler enhancements: Introduced explicit error types for untranspilable muGraphs and integrated them into transpilation results, and added a constraint that each stensor has at most one swizzled dimension to improve layout resolution. These changes, tracked in commit 2071b146ad1f98f4f09947c9f4f5451dd5ae1776, reduce debugging time and increase transpile reliability, enabling faster feature delivery and more predictable builds.
1 Commits • 1 Features
Nov 1, 2024November 2024 - Mirage transpiler enhancements: Introduced explicit error types for untranspilable muGraphs and integrated them into transpilation results, and added a constraint that each stensor has at most one swizzled dimension to improve layout resolution. These changes, tracked in commit 2071b146ad1f98f4f09947c9f4f5451dd5ae1776, reduce debugging time and increase transpile reliability, enabling faster feature delivery and more predictable builds.
November 2024
October 2024
3 Commits • 1 Features
Oct 1, 2024October 2024 performance summary for mirage-project/mirage: Delivered a major optimization to the transpiler cost model by integrating shared memory usage into tensor layout decisions, with constants refined and a shared memory cost factor added to improve memory-bound performance. Released version 0.2.2 to stabilize packaging and initialization, updating setup scripts accordingly. Completed a user-facing demo improvement fix by correcting a typo and adding a required torch import in the demo group query attention script, enhancing reliability of demos and demonstrations.
October 2024
3 Commits • 1 Features
Oct 1, 2024October 2024 performance summary for mirage-project/mirage: Delivered a major optimization to the transpiler cost model by integrating shared memory usage into tensor layout decisions, with constants refined and a shared memory cost factor added to improve memory-bound performance. Released version 0.2.2 to stabilize packaging and initialization, updating setup scripts accordingly. Completed a user-facing demo improvement fix by correcting a typo and adding a required torch import in the demo group query attention script, enhancing reliability of demos and demonstrations.
Activity
Loading activity data...
Quality Metrics
Correctness85.2%
Maintainability84.6%
Architecture84.6%
Performance81.4%
AI Usage20.4%
Skills & Technologies
Programming Languages
BashC++CUDACUDA CCythonDoxygenMarkdownPythonRSTShell
Technical Skills
Backend DevelopmentBatchingBenchmarkingBug FixingBuild ConfigurationBuild ManagementBuild System ConfigurationBuild SystemsC++C++ DevelopmentCUDACUDA DevelopmentCUDA Kernel DevelopmentCUDA ProgrammingCUDA programming
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline