StreamHPC/rocm-libraries
Aug 2025 – Aug 2025
1 Month active
Languages Used
C++Markdown
Technical Skills
GPU ProgrammingHigh-Performance ComputingKernel DevelopmentLinear AlgebraQuantization
Vijay Krish
PROFILE
Vijay Krish
Vijay Krishnan developed the CK_TILE kernel for GEMM operations in the StreamHPC/rocm-libraries repository, focusing on groupwise quantization of the B tensor to enhance low-precision matrix multiplication. He implemented a technical approach that loads scale tensors into registers for efficient dequantization and enables quantization from either A or B operands, increasing flexibility. His work introduced new pipelines using an Intrawave scheduler and block GEMM primitives, supporting data types such as fp8, bf8, and i4. Leveraging C++ and expertise in GPU programming and kernel development, Vijay delivered a deep, foundational feature that broadens quantization strategies for high-performance computing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,497
Activity Months1
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2482 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025: Delivered the CK_TILE kernel for GEMM with groupwise quantization of the B tensor, enabling dequantization by loading scale tensors into registers and allowing quantization from either A or B operands. Implemented new pipelines with an Intrawave scheduler and block GEMM primitives to support multiple data-type combinations, including fp8/bf8 with i4. This work improves low-precision GEMM performance, enhances quantization flexibility, and lays the groundwork for broader quantization strategies in StreamHPC/rocm-libraries.
1 Commits • 1 Features
Aug 1, 2025August 2025: Delivered the CK_TILE kernel for GEMM with groupwise quantization of the B tensor, enabling dequantization by loading scale tensors into registers and allowing quantization from either A or B operands. Implemented new pipelines with an Intrawave scheduler and block GEMM primitives to support multiple data-type combinations, including fp8/bf8 with i4. This work improves low-precision GEMM performance, enhances quantization flexibility, and lays the groundwork for broader quantization strategies in StreamHPC/rocm-libraries.
August 2025
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Markdown
Technical Skills
GPU ProgrammingHigh-Performance ComputingKernel DevelopmentLinear AlgebraQuantization
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