tenstorrent/tt-mlir
Nov 2024 – Dec 2024
2 Months active
Languages Used
C++MLIRCMakeScript
Technical Skills
Compiler DevelopmentEmbedded SystemsLow-Level OptimizationMLIR DialectsMemory ManagementAlgorithm Design
Filip Bajraktari
PROFILE
Filip Bajraktari
During a two-month period, Fatos Bajraktari developed and refined the L1 Interleaved Memory Layout Policy in the tenstorrent/tt-mlir repository, focusing on scalable memory management for TTNN workloads. He refactored the backend to support multiple memory policies, enabling flexible optimization strategies. Leveraging C++, MLIR, and advanced compiler development skills, Fatos implemented a greedy join-node optimization and introduced the BFInterleavedPolicy to handle fork-join scenarios. His work included enhancing memory layout calculations and shard shapes for L1 interleaved tensors, improving data locality and tiling efficiency. The depth of these contributions laid a robust foundation for future backend memory optimizations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
2,464
Activity Months2
Your Network
438 peopleWork History
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for tenstorrent/tt-mlir focusing on L1 Interleaved Memory Layout Policy Improvements and their business value.
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for tenstorrent/tt-mlir focusing on L1 Interleaved Memory Layout Policy Improvements and their business value.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key feature delivered: TTNN Memory Layout Policy: L1 Interleaved in the TT-MLIR backend, with a refactor to support multiple memory policies. This enables flexible memory management and optimization for TTNN workloads, improving scalability and resource efficiency. Delivered via commit c038025ed0bf7d71f0c09fb3e47ce2c936ba76e2 ("L1 interleaved policy (#1117)"), enabling future policy experiments and broader TTNN backend optimization.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key feature delivered: TTNN Memory Layout Policy: L1 Interleaved in the TT-MLIR backend, with a refactor to support multiple memory policies. This enables flexible memory management and optimization for TTNN workloads, improving scalability and resource efficiency. Delivered via commit c038025ed0bf7d71f0c09fb3e47ce2c936ba76e2 ("L1 interleaved policy (#1117)"), enabling future policy experiments and broader TTNN backend optimization.
Activity
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Quality Metrics
Correctness85.0%
Maintainability82.6%
Architecture87.6%
Performance82.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CMakeScriptMLIR
Technical Skills
Algorithm DesignC++Compiler DevelopmentCompiler OptimizationEmbedded SystemsLow-Level OptimizationMLIRMLIR DialectsMemory ManagementOptimizer DevelopmentTensor Operations
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