tenstorrent/tt-metal
Dec 2024 – Oct 2025
11 Months active
Languages Used
C++PythonC
Technical Skills
C++ programmingPythonPython programmingdata movementdata movement optimizationend-to-end testing
Ligang Long
PROFILE
Ligang Long
Llong contributed to the tenstorrent/tt-metal repository by engineering high-performance tensor operations and distributed compute features for large-scale machine learning workloads. Over 11 months, Llong developed and optimized core components such as multi-core tensor slicing, fused All-Reduce and QKV attention kernels, and robust data movement paths, leveraging C++, CUDA, and Python. Their work included low-level memory management, parallel programming, and kernel development to improve throughput, reliability, and scalability. By addressing edge-case bugs and integrating production-ready kernels, Llong enabled predictable, high-throughput data flows and reduced maintenance overhead, demonstrating deep technical proficiency in performance optimization and distributed systems engineering.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
181Total
Bugs
28
Commits
181
Features
67
Lines of code
30,312
Activity Months11
Your Network
742 people
Work History
October 2025
1 Commits
Oct 1, 2025Month 2025-10 — tenstorrent/tt-metal: Focused stabilization and reliability improvements in the sampling path by addressing the async NOC read alignment issue for sampling operations. Delivered a targeted bug fix that enhances data movement reliability and performance across all cores, reducing edge-case failures and smoothing high-concurrency workloads. Implemented adjustments to memory access patterns and expanded buffer sizes, culminating in a patch tied to a concrete commit. Key patch: bec11e4e5bfd06269f89f1c2f0573aa9eef58a67 with message: "fix async_noc_read alignment issue for sampling. (#29752)". Impact: More robust sampling path, lower risk of stalls, and easier integration with existing test suites. Demonstrated proficiency in low-level systems programming, memory management, and concurrency. Business value includes improved stability for throughput-critical data movement, enabling more predictable production performance and reduced maintenance overhead.
1 Commits
Oct 1, 2025Month 2025-10 — tenstorrent/tt-metal: Focused stabilization and reliability improvements in the sampling path by addressing the async NOC read alignment issue for sampling operations. Delivered a targeted bug fix that enhances data movement reliability and performance across all cores, reducing edge-case failures and smoothing high-concurrency workloads. Implemented adjustments to memory access patterns and expanded buffer sizes, culminating in a patch tied to a concrete commit. Key patch: bec11e4e5bfd06269f89f1c2f0573aa9eef58a67 with message: "fix async_noc_read alignment issue for sampling. (#29752)". Impact: More robust sampling path, lower risk of stalls, and easier integration with existing test suites. Demonstrated proficiency in low-level systems programming, memory management, and concurrency. Business value includes improved stability for throughput-critical data movement, enabling more predictable production performance and reduced maintenance overhead.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 Monthly Summary for tenstorrent/tt-metal focusing on feature delivery and production readiness. Key outcomes include the Tensor multi-core slicing operation with multi-type/stride support and production integration of bench-generated kernel code.
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025 Monthly Summary for tenstorrent/tt-metal focusing on feature delivery and production readiness. Key outcomes include the Tensor multi-core slicing operation with multi-type/stride support and production integration of bench-generated kernel code.
August 2025
57 Commits • 22 Features
Aug 1, 2025August 2025 TT-Metal monthly performance review: focused on multicast path optimizations and MM compute improvements, with emphasis on performance, stability, and maintainability across the codebase. Work included disciplined experimentation, feature delivery, and targeted refactors to support scalable, low-latency compute pipelines for large-scale workloads.
57 Commits • 22 Features
Aug 1, 2025August 2025 TT-Metal monthly performance review: focused on multicast path optimizations and MM compute improvements, with emphasis on performance, stability, and maintainability across the codebase. Work included disciplined experimentation, feature delivery, and targeted refactors to support scalable, low-latency compute pipelines for large-scale workloads.
August 2025
July 2025
67 Commits • 32 Features
Jul 1, 2025Month: 2025-07 — Delivered a focused set of features, reliability fixes, and performance optimizations in tt-metal to unlock higher throughput, lower latency, and better scalability for distributed LLAMA workloads. Emphasized business value through improved synchronization, more efficient data paths, and robust program factory wiring for AGMM workflows.
July 2025
67 Commits • 32 Features
Jul 1, 2025Month: 2025-07 — Delivered a focused set of features, reliability fixes, and performance optimizations in tt-metal to unlock higher throughput, lower latency, and better scalability for distributed LLAMA workloads. Emphasized business value through improved synchronization, more efficient data paths, and robust program factory wiring for AGMM workflows.
June 2025
21 Commits • 2 Features
Jun 1, 2025June 2025 performance-focused month for tt-metal: Delivered key features enabling scalable distributed training and fixed a set of stability and correctness issues in the data path. Focused on improving performance, reliability, and maintainability through targeted bug fixes and feature work.
21 Commits • 2 Features
Jun 1, 2025June 2025 performance-focused month for tt-metal: Delivered key features enabling scalable distributed training and fixed a set of stability and correctness issues in the data path. Focused on improving performance, reliability, and maintainability through targeted bug fixes and feature work.
June 2025
May 2025
15 Commits • 2 Features
May 1, 2025May 2025 focused on delivering high-impact QKV attention optimizations for Llama3 in tt-metal and hardening Q layout support for broader reliability. Implemented QKV fuse for reduced-scatter to build QKV heads and introduced a tilized Q tensor path, achieving lower kernel time and higher attention throughput for Llama3 workloads. Added row-major Q tensor layout across attention and SDPA paths, expanded unit/integration tests to cover both row-major and tile Q layouts, and adjusted memory configurations to validate performance and correctness. Fixed critical initialization-order issues and addressed SDPA-related unit-test failures; performed code cleanup to stabilize CI. This work increases inference throughput, improves testing coverage, and demonstrates advanced GPU kernel optimization, memory layout experimentation, and test-driven development.
May 2025
15 Commits • 2 Features
May 1, 2025May 2025 focused on delivering high-impact QKV attention optimizations for Llama3 in tt-metal and hardening Q layout support for broader reliability. Implemented QKV fuse for reduced-scatter to build QKV heads and introduced a tilized Q tensor path, achieving lower kernel time and higher attention throughput for Llama3 workloads. Added row-major Q tensor layout across attention and SDPA paths, expanded unit/integration tests to cover both row-major and tile Q layouts, and adjusted memory configurations to validate performance and correctness. Fixed critical initialization-order issues and addressed SDPA-related unit-test failures; performed code cleanup to stabilize CI. This work increases inference throughput, improves testing coverage, and demonstrates advanced GPU kernel optimization, memory layout experimentation, and test-driven development.
April 2025
8 Commits • 2 Features
Apr 1, 2025April 2025 performance-focused delivery for tenstorrent/tt-metal. Implemented fused All-Reduce + QKV heads optimization with end-to-end performance validation, and introduced performance testing for LlamaReduceScatter. These efforts deliver measurable throughput gains, improved transformer efficiency, and enhanced observability for scaling workloads across models.
8 Commits • 2 Features
Apr 1, 2025April 2025 performance-focused delivery for tenstorrent/tt-metal. Implemented fused All-Reduce + QKV heads optimization with end-to-end performance validation, and introduced performance testing for LlamaReduceScatter. These efforts deliver measurable throughput gains, improved transformer efficiency, and enhanced observability for scaling workloads across models.
April 2025
March 2025
2 Commits
Mar 1, 2025March 2025 monthly summary for tenstorrent/tt-metal focused on stability and performance improvements in kernel padding and tensor alignment. Delivered targeted fixes to memory management and architecture-specific alignment, reducing memory pressure, improving data flow, and broadening hardware compatibility. Resulted in more reliable large-tensor padding workflows and consistent behavior across platforms, enabling smoother production workloads.
March 2025
2 Commits
Mar 1, 2025March 2025 monthly summary for tenstorrent/tt-metal focused on stability and performance improvements in kernel padding and tensor alignment. Delivered targeted fixes to memory management and architecture-specific alignment, reducing memory pressure, improving data flow, and broadening hardware compatibility. Resulted in more reliable large-tensor padding workflows and consistent behavior across platforms, enabling smoother production workloads.
February 2025
4 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for tenstorrent/tt-metal focused on delivering test coverage, reliability, and architecture improvements that support higher performance and stability in BH deployments. Key work included Python test porting for TTNN, alignment improvements for memory allocators, safeguards to prevent divide-by-zero in sweeps, and a direct-shard refactor to enhance device handling. These changes collectively reduce risk, improve transfer reliability, and strengthen testing accuracy for future optimizations.
4 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for tenstorrent/tt-metal focused on delivering test coverage, reliability, and architecture improvements that support higher performance and stability in BH deployments. Key work included Python test porting for TTNN, alignment improvements for memory allocators, safeguards to prevent divide-by-zero in sweeps, and a direct-shard refactor to enhance device handling. These changes collectively reduce risk, improve transfer reliability, and strengthen testing accuracy for future optimizations.
February 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered a foundational memory-path optimization in the tt-metal repository by enabling an efficient DRAM-to-L1 data copy via a scratchpad, focusing on robust handling of unaligned data transfers to reduce copy overhead and boost throughput. This work strengthens the core memory path, enabling more predictable performance for memory-bound workloads and serving as a baseline for further memory subsystem optimizations.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Delivered a foundational memory-path optimization in the tt-metal repository by enabling an efficient DRAM-to-L1 data copy via a scratchpad, focusing on robust handling of unaligned data transfers to reduce copy overhead and boost throughput. This work strengthens the core memory path, enabling more predictable performance for memory-bound workloads and serving as a baseline for further memory subsystem optimizations.
December 2024
4 Commits • 2 Features
Dec 1, 2024December 2024 performance highlights for tenstorrent/tt-metal: Delivered core tensor data movement optimizations and expanded padding capabilities, plus introduced robust end-to-end testing to protect data paths under adversarial conditions. These workstreams improved L1 data movement efficiency for tensor ops (e.g., maxpooling, dilation) and increased reliability of interleaved_to_sharded and sharded_to_interleaved flows, delivering measurable business value in throughput, predictability, and resilience.
4 Commits • 2 Features
Dec 1, 2024December 2024 performance highlights for tenstorrent/tt-metal: Delivered core tensor data movement optimizations and expanded padding capabilities, plus introduced robust end-to-end testing to protect data paths under adversarial conditions. These workstreams improved L1 data movement efficiency for tensor ops (e.g., maxpooling, dilation) and increased reliability of interleaved_to_sharded and sharded_to_interleaved flows, delivering measurable business value in throughput, predictability, and resilience.
December 2024
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Quality Metrics
Correctness86.2%
Maintainability82.0%
Architecture84.4%
Performance83.0%
AI Usage26.4%
Skills & Technologies
Programming Languages
CC++Python
Technical Skills
API designAPI integrationAlgorithm optimizationAsynchronous ProgrammingAsynchronous programmingC++C++ DevelopmentC++ developmentC++ programmingCUDACUDA ProgrammingCUDA programmingCode RefactoringCode optimizationCode refactoring
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