iree-org/wave
Oct 2024 – Oct 2025
13 Months active
Languages Used
PythonC++MLIRCMakeRSTShellreStructuredTextPyTorch
Technical Skills
Code OptimizationCompiler DevelopmentEnum UsageGPU ProgrammingMachine Learning KernelsPerformance Optimization
harsh-nod
PROFILE
Harsh-nod
Harsh Menon developed advanced kernel scheduling, attention mechanisms, and performance tooling for the iree-org/wave repository, focusing on scalable deep learning workloads. He engineered explicit kernel scheduling and pipelining, integrated a Rust-based performance extension, and implemented dynamic shape support in the IndexMapping API. Leveraging C++, Python, and MLIR, Harsh introduced an AMDGCN assembly backend, optimized memory allocation, and enabled cross-platform builds. His work included robust CI/CD automation, detailed benchmarking, and documentation improvements, resulting in reproducible scheduling, improved GPU throughput, and maintainable code. The depth of his contributions addressed both low-level optimization and high-level usability, supporting reliable, high-performance kernel development.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
129Total
Bugs
7
Commits
129
Features
53
Lines of code
76,922
Activity Months13
Your Network
47 peopleShared Repositories
47
Aaron St GeorgeMember
Aditya KankariyaMember
Aditya KankariyaMember
Oleksandr "Alex" ZinenkoMember
Andrew WoloszynMember
Aurore De SpirletMember
Avinash SharmaMember
Oleksandr "Alex" ZinenkoMember
Boian PetkantchinMember
Work History
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 — Wave project (iree-org/wave) delivered high-impact features, targeted bug fixes, and expanded hardware-targeting capabilities, reinforcing performance, reliability, and developer experience. Key initiatives focused on explicit kernel scheduling, AMDGPU backend support, and documentation quality to reduce maintenance overhead.
3 Commits • 2 Features
Oct 1, 2025October 2025 — Wave project (iree-org/wave) delivered high-impact features, targeted bug fixes, and expanded hardware-targeting capabilities, reinforcing performance, reliability, and developer experience. Key initiatives focused on explicit kernel scheduling, AMDGPU backend support, and documentation quality to reduce maintenance overhead.
October 2025
September 2025
3 Commits • 3 Features
Sep 1, 20252025-09 Monthly performance summary for iree-org/wave: 1) Key features delivered - Mi35x GPU CI integration and workflow refactor: added mi35x CI runner and refactored common CI expressions into environment variables to improve testing coverage and maintainability across pipelines. Commit 02473ff971660703bddb02ecc0302689c5d92fbc. - Attention scheduling performance optimization for PREFETCH_ATTENTION (8 waves): introduced conditional barriers and cluster/reorder scheduling to enable a ping-pong execution strategy, boosting data movement efficiency and compute throughput. Commit e07864a1e50920ed497ef813f01f09bf3a692b41. - Documentation cleanup: removed migration notice from README to reflect completion and current repo state. Commit 09ba624bb02b37342d940bfad6d2deaaf89bd78d. 2) Major bugs fixed - No explicit bug fixes documented this month; focus was on feature delivery and maintainability improvements. 3) Overall impact and accomplishments - Improved CI reliability and GPU testing coverage for Mi35x workflows, enabling faster feedback loops for GPU-related changes. - Enhanced attention computation performance with 8-wave scheduling, contributing to better throughput and reduced latency in large-scale runs. - Documentation alignment reduces onboarding friction and clarifies repo state. 4) Technologies/skills demonstrated - CI/CD automation, GPU-specific CI workflows, environment-variable configuration, scheduling and data movement optimization, documentation hygiene, and change traceability through detailed commit messages.
September 2025
3 Commits • 3 Features
Sep 1, 20252025-09 Monthly performance summary for iree-org/wave: 1) Key features delivered - Mi35x GPU CI integration and workflow refactor: added mi35x CI runner and refactored common CI expressions into environment variables to improve testing coverage and maintainability across pipelines. Commit 02473ff971660703bddb02ecc0302689c5d92fbc. - Attention scheduling performance optimization for PREFETCH_ATTENTION (8 waves): introduced conditional barriers and cluster/reorder scheduling to enable a ping-pong execution strategy, boosting data movement efficiency and compute throughput. Commit e07864a1e50920ed497ef813f01f09bf3a692b41. - Documentation cleanup: removed migration notice from README to reflect completion and current repo state. Commit 09ba624bb02b37342d940bfad6d2deaaf89bd78d. 2) Major bugs fixed - No explicit bug fixes documented this month; focus was on feature delivery and maintainability improvements. 3) Overall impact and accomplishments - Improved CI reliability and GPU testing coverage for Mi35x workflows, enabling faster feedback loops for GPU-related changes. - Enhanced attention computation performance with 8-wave scheduling, contributing to better throughput and reduced latency in large-scale runs. - Documentation alignment reduces onboarding friction and clarifies repo state. 4) Technologies/skills demonstrated - CI/CD automation, GPU-specific CI workflows, environment-variable configuration, scheduling and data movement optimization, documentation hygiene, and change traceability through detailed commit messages.
August 2025
8 Commits • 5 Features
Aug 1, 2025August 2025: Delivered performance-focused enhancements and reliability improvements for iree-org/wave. Key features include a Rust-based performance extension integrated into the Wave Python package, dynamic variables in the IndexMapping API enabling dynamic shapes and indices, and enhanced attention kernel scheduling with modulo scheduling plus a 4-stage prefetch and multi-buffering strategy. Documentation stability improvements fixed ReadTheDocs build failures by adding Rust tooling and aligning metadata with PyPI. Internal test improvements and clearer indexing context usage contributed to more robust kernel tests and maintainable code. These efforts collectively improve runtime performance, robustness, and developer experience, enabling faster, more flexible kernels and smoother releases.
8 Commits • 5 Features
Aug 1, 2025August 2025: Delivered performance-focused enhancements and reliability improvements for iree-org/wave. Key features include a Rust-based performance extension integrated into the Wave Python package, dynamic variables in the IndexMapping API enabling dynamic shapes and indices, and enhanced attention kernel scheduling with modulo scheduling plus a 4-stage prefetch and multi-buffering strategy. Documentation stability improvements fixed ReadTheDocs build failures by adding Rust tooling and aligning metadata with PyPI. Internal test improvements and clearer indexing context usage contributed to more robust kernel tests and maintainable code. These efforts collectively improve runtime performance, robustness, and developer experience, enabling faster, more flexible kernels and smoother releases.
August 2025
July 2025
28 Commits • 7 Features
Jul 1, 2025July 2025 performance highlights for iree-org/wave: Delivered a major namespace refactor migrating iree.turbine modules to the new wave and wave_lang namespaces, consolidating dynamo, transforms, tools, aot, runtime, kernel, and support; introduced an outer loop for schedule search; strengthened build and packaging with runtime included in setup.py and cross-platform fixes for macOS and Windows; improved CI reliability through test cleanup and visualization fixes; refreshed and expanded documentation including quickstart and thread trace visibility to accelerate onboarding and usage. These changes yield improved maintainability, faster iteration, easier deployments, and clearer developer guidance, supporting faster time-to-value for consumers and contributors.
July 2025
28 Commits • 7 Features
Jul 1, 2025July 2025 performance highlights for iree-org/wave: Delivered a major namespace refactor migrating iree.turbine modules to the new wave and wave_lang namespaces, consolidating dynamo, transforms, tools, aot, runtime, kernel, and support; introduced an outer loop for schedule search; strengthened build and packaging with runtime included in setup.py and cross-platform fixes for macOS and Windows; improved CI reliability through test cleanup and visualization fixes; refreshed and expanded documentation including quickstart and thread trace visibility to accelerate onboarding and usage. These changes yield improved maintainability, faster iteration, easier deployments, and clearer developer guidance, supporting faster time-to-value for consumers and contributors.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 — iree-org/wave: Delivered scheduling subsystem enhancements that improve reproducibility, configurability, and reliability of kernel scheduling. Implemented import/export of wave kernel schedules with a human-readable schedule file format and compile-time options to override or dump schedules. Introduced ScheduleValidator to adjust schedules while enforcing dependency constraints and resource limits, including tracking resource usage and repair of violations. Impact: Reduced manual tuning time, enabled safer production deployments, and provided end-to-end visibility into scheduling decisions. Relevant repository: iree-org/wave.
2 Commits • 1 Features
Jun 1, 2025June 2025 — iree-org/wave: Delivered scheduling subsystem enhancements that improve reproducibility, configurability, and reliability of kernel scheduling. Implemented import/export of wave kernel schedules with a human-readable schedule file format and compile-time options to override or dump schedules. Introduced ScheduleValidator to adjust schedules while enforcing dependency constraints and resource limits, including tracking resource usage and repair of violations. Impact: Reduced manual tuning time, enabled safer production deployments, and provided end-to-end visibility into scheduling decisions. Relevant repository: iree-org/wave.
June 2025
May 2025
11 Commits • 6 Features
May 1, 2025May 2025 performance-focused month for iree-org/wave. Key features delivered include a Wave benchmarking framework with cross-kernel performance comparison (benchmarking script for wave_sdpa kernel and comparison against flash_attn_func), enabling CI-driven performance analysis across shapes and implementations; a GEMM kernel tutorial notebook with installation, kernel definition, and a PyTorch-based validation test; Wave documentation improvements with Mermaid diagrams support; a compiler memory allocation optimization that minimizes shared allocations via live-range analysis with safety checks; MLIR-style printing utilities for fx graphs to enhance readability; and a Rust-based All-Pairs Longest Path (APLP) scheduling optimization with pruning logic and parallel Floyd-Warshall using Rayon. Major bugs fixed include Lit test suite stability for Wave kernel codegen and GEMM. Overall impact and accomplishments include improved performance visibility and CI-driven decision-making, more reliable tests, enhanced developer experience for kernel work, and groundwork for scalable scheduling optimizations. Technologies/skills demonstrated include Python-based benchmarking and CI integration, Jupyter notebook validation, MLIR tooling and diagrams, Mermaid diagram support, Rust (with Rayon) for optimization, and memory-performance engineering.
May 2025
11 Commits • 6 Features
May 1, 2025May 2025 performance-focused month for iree-org/wave. Key features delivered include a Wave benchmarking framework with cross-kernel performance comparison (benchmarking script for wave_sdpa kernel and comparison against flash_attn_func), enabling CI-driven performance analysis across shapes and implementations; a GEMM kernel tutorial notebook with installation, kernel definition, and a PyTorch-based validation test; Wave documentation improvements with Mermaid diagrams support; a compiler memory allocation optimization that minimizes shared allocations via live-range analysis with safety checks; MLIR-style printing utilities for fx graphs to enhance readability; and a Rust-based All-Pairs Longest Path (APLP) scheduling optimization with pruning logic and parallel Floyd-Warshall using Rayon. Major bugs fixed include Lit test suite stability for Wave kernel codegen and GEMM. Overall impact and accomplishments include improved performance visibility and CI-driven decision-making, more reliable tests, enhanced developer experience for kernel work, and groundwork for scalable scheduling optimizations. Technologies/skills demonstrated include Python-based benchmarking and CI integration, Jupyter notebook validation, MLIR tooling and diagrams, Mermaid diagram support, Rust (with Rayon) for optimization, and memory-performance engineering.
April 2025
15 Commits • 7 Features
Apr 1, 2025During 2025-04, the Wave project delivered significant kernel and tooling enhancements that directly improve inference throughput, precision flexibility, and reliability. Key features include sliding window attention, GQA/MQA vanilla attention with dedicated decode kernel, and a fast-math option to unlock aggressive FP optimizations. We also added a modernized attention API (wave_sdpa) with in-kernel scaling to reduce latency, and speculative decoding support with PyTorch integration for multi-iteration generation. Reliability improvements included targeted bug fixes in the GQA kernel and test infrastructure upgrades (MLIR enablement and hardware gating) to ensure stable performance across environments.
15 Commits • 7 Features
Apr 1, 2025During 2025-04, the Wave project delivered significant kernel and tooling enhancements that directly improve inference throughput, precision flexibility, and reliability. Key features include sliding window attention, GQA/MQA vanilla attention with dedicated decode kernel, and a fast-math option to unlock aggressive FP optimizations. We also added a modernized attention API (wave_sdpa) with in-kernel scaling to reduce latency, and speculative decoding support with PyTorch integration for multi-iteration generation. Reliability improvements included targeted bug fixes in the GQA kernel and test infrastructure upgrades (MLIR enablement and hardware gating) to ensure stable performance across environments.
April 2025
March 2025
12 Commits • 4 Features
Mar 1, 2025March 2025 monthly summary for iree-org/wave: Delivered substantial performance tooling, kernel-level optimizations, and runtime architecture improvements that directly enhance benchmarking fidelity, runtime efficiency, and ease of integration. Key outcomes include enhanced profiling and trace-capable benchmarking, auto-tuning and kernel launch optimizations, a major overhaul of the Wave runtime with improved Python-C++ data transfer, caching, and grid management, and targeted documentation/licensing updates to improve adoption and compliance. The work strengthened performance analysis capabilities, reduced kernel launch overhead, and improved maintainability and deployment options (including Torch-based runtime packaging).
March 2025
12 Commits • 4 Features
Mar 1, 2025March 2025 monthly summary for iree-org/wave: Delivered substantial performance tooling, kernel-level optimizations, and runtime architecture improvements that directly enhance benchmarking fidelity, runtime efficiency, and ease of integration. Key outcomes include enhanced profiling and trace-capable benchmarking, auto-tuning and kernel launch optimizations, a major overhaul of the Wave runtime with improved Python-C++ data transfer, caching, and grid management, and targeted documentation/licensing updates to improve adoption and compliance. The work strengthened performance analysis capabilities, reduced kernel launch overhead, and improved maintainability and deployment options (including Torch-based runtime packaging).
February 2025
15 Commits • 3 Features
Feb 1, 2025February 2025 performance summary for iree-org/wave: Delivered high-impact kernel improvements across attention, caching, and memory pathways, with a focus on performance, scalability, and stability. Implemented extend attention with chunked prefill, dynamic sequence dimensions, and causal masking, augmented MFMA intrinsic support, and reorganized tests/benchmarks to improve measurement accuracy. Optimized kernel hashing and runtime caching (including physical layout in cache keys and an LRU-based hash cache) along with runtime caches for system contexts/VM functions. Improved memory bandwidth by transforming global gathers to shared memory. Reverted the Alibi Attention change to restore prior behavior, ensuring stability. These changes collectively elevate throughput, reduce latency, and enable longer-context workloads while maintaining reliable test coverage.
15 Commits • 3 Features
Feb 1, 2025February 2025 performance summary for iree-org/wave: Delivered high-impact kernel improvements across attention, caching, and memory pathways, with a focus on performance, scalability, and stability. Implemented extend attention with chunked prefill, dynamic sequence dimensions, and causal masking, augmented MFMA intrinsic support, and reorganized tests/benchmarks to improve measurement accuracy. Optimized kernel hashing and runtime caching (including physical layout in cache keys and an LRU-based hash cache) along with runtime caches for system contexts/VM functions. Improved memory bandwidth by transforming global gathers to shared memory. Reverted the Alibi Attention change to restore prior behavior, ensuring stability. These changes collectively elevate throughput, reduce latency, and enable longer-context workloads while maintaining reliable test coverage.
February 2025
January 2025
9 Commits • 5 Features
Jan 1, 2025January 2025 performance summary for iree-org/wave: Delivered substantial kernel and memory model enhancements enabling more robust attention computation and scalable decoding pipelines across PDA-driven workloads. Strengthened reliability through test infrastructure improvements and critical test fixes, driving higher stability in release builds and CI.
January 2025
9 Commits • 5 Features
Jan 1, 2025January 2025 performance summary for iree-org/wave: Delivered substantial kernel and memory model enhancements enabling more robust attention computation and scalable decoding pipelines across PDA-driven workloads. Strengthened reliability through test infrastructure improvements and critical test fixes, driving higher stability in release builds and CI.
December 2024
9 Commits • 5 Features
Dec 1, 2024December 2024 — iree-org/wave: Delivered key features to accelerate transformer workloads, improved attention throughput, and strengthened correctness and configurability. Major bets were on a performant Evoformer kernel, advanced attention decoding with a two-phase path, and configurable wave kernel compilation, underpinned by now-stable indexing and thread-shape analysis. Key features delivered: - Evoformer kernel implementation with bf16 support: dedicated kernel template, detailed constraints, index mappings, and reduction loop to enable efficient transformer components. - Attention decoding enhancements (decode phase and flash decode v2): new decode kernel (no KV cache) plus two-phase attention with kernels for QK computation and softmax with V multiplication to boost attention throughput and modularity. - Wave kernel configuration flag support: waves_per_eu flag supported in compilation with tests covering the new configuration. - Indexing and thread shape analysis improvements: clarified index propagation logic, simplified contiguity computation, and consolidated thread-shape analysis into index sequence analysis; included approximate_difference contiguity checks and fixed related type annotations. - Added ml_dtypes dependency: ensures proper data type support as a new runtime dependency. Major bugs fixed: - Correctness and stability improvements across indexing propagation and contiguity checks; fixed type annotation issues; expanded test coverage for the new waves_per_eu flag to prevent misconfigurations. Overall impact and accomplishments: - Increased transformer throughput and reliability through optimized kernels and robust configuration, enabling scalable inference and easier maintenance. The work reduces risk associated with new flags and data-path changes while delivering measurable performance gains in transformer components. Technologies/skills demonstrated: - bf16 support, kernel templating, multi-kernel attention path, performance optimization, test-driven validation, dependency management (ml_dtypes), and code quality improvements.
9 Commits • 5 Features
Dec 1, 2024December 2024 — iree-org/wave: Delivered key features to accelerate transformer workloads, improved attention throughput, and strengthened correctness and configurability. Major bets were on a performant Evoformer kernel, advanced attention decoding with a two-phase path, and configurable wave kernel compilation, underpinned by now-stable indexing and thread-shape analysis. Key features delivered: - Evoformer kernel implementation with bf16 support: dedicated kernel template, detailed constraints, index mappings, and reduction loop to enable efficient transformer components. - Attention decoding enhancements (decode phase and flash decode v2): new decode kernel (no KV cache) plus two-phase attention with kernels for QK computation and softmax with V multiplication to boost attention throughput and modularity. - Wave kernel configuration flag support: waves_per_eu flag supported in compilation with tests covering the new configuration. - Indexing and thread shape analysis improvements: clarified index propagation logic, simplified contiguity computation, and consolidated thread-shape analysis into index sequence analysis; included approximate_difference contiguity checks and fixed related type annotations. - Added ml_dtypes dependency: ensures proper data type support as a new runtime dependency. Major bugs fixed: - Correctness and stability improvements across indexing propagation and contiguity checks; fixed type annotation issues; expanded test coverage for the new waves_per_eu flag to prevent misconfigurations. Overall impact and accomplishments: - Increased transformer throughput and reliability through optimized kernels and robust configuration, enabling scalable inference and easier maintenance. The work reduces risk associated with new flags and data-path changes while delivering measurable performance gains in transformer components. Technologies/skills demonstrated: - bf16 support, kernel templating, multi-kernel attention path, performance optimization, test-driven validation, dependency management (ml_dtypes), and code quality improvements.
December 2024
November 2024
11 Commits • 3 Features
Nov 1, 2024November 2024 — iree-org/wave monthly summary focusing on key accomplishments, features delivered, major bugs fixed, impact, and skills demonstrated. This month centered on strengthening Wave Kernel robust attention with dynamic shapes, enabling dynamic GEMMs, and stabilizing CI/test pipelines to improve throughput, reliability, and developer productivity.
November 2024
11 Commits • 3 Features
Nov 1, 2024November 2024 — iree-org/wave monthly summary focusing on key accomplishments, features delivered, major bugs fixed, impact, and skills demonstrated. This month centered on strengthening Wave Kernel robust attention with dynamic shapes, enabling dynamic GEMMs, and stabilizing CI/test pipelines to improve throughput, reliability, and developer productivity.
October 2024
3 Commits • 2 Features
Oct 1, 2024Month: 2024-10 — iree-org/wave: Focused on performance optimization and flexibility in kernel operations. Delivered kernel reduction and indexing optimizations and added reshape support for wave kernels. These changes improve verification, indexing accuracy, and runtime performance, while broadening test coverage and reducing edge-case risks. Minor refactoring and cleanups contributed to stability and maintainability.
3 Commits • 2 Features
Oct 1, 2024Month: 2024-10 — iree-org/wave: Focused on performance optimization and flexibility in kernel operations. Delivered kernel reduction and indexing optimizations and added reshape support for wave kernels. These changes improve verification, indexing accuracy, and runtime performance, while broadening test coverage and reducing edge-case risks. Minor refactoring and cleanups contributed to stability and maintainability.
October 2024
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Quality Metrics
Correctness90.6%
Maintainability87.8%
Architecture89.2%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashC++CMakeJupyter NotebookMLIRMarkdownMermaidPyTorchPythonRST
Technical Skills
AMDGCN AssemblyAPI DesignAPI UsageAlgorithm DesignAlgorithm ImplementationAlgorithm OptimizationAttention MechanismAttention MechanismsBackend DevelopmentBenchmarkingBug FixingBuild System ConfigurationBuild System ManagementBuild SystemsC++
Repositories Contributed To
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