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shivampr

PROFILE

Shivampr

Shivam Prasad contributed to jeejeelee/vllm and duckdb/pg_duckdb by building features and improving reliability in large language model inference and database systems. He optimized Triton kernel configurations for Qwen3-30B on H100 GPUs, tuning parameters in C++ and Python to enhance throughput and latency for enterprise inference workloads. In DuckDB, he developed a suite of map functions in C and SQL, expanding support for complex data types and analytics. Shivam also improved memory management and error handling in vllm, adding robust out-of-memory detection and kernel fallback logic, demonstrating depth in kernel development, performance optimization, and backend engineering.

Overall Statistics

Feature vs Bugs

75%Features

Repository Contributions

4Total
Bugs
1
Commits
4
Features
3
Lines of code
1,438
Activity Months3

Your Network

1275 people

Work History

December 2025

2 Commits • 1 Features

Dec 1, 2025

December 2025 monthly summary for jeejeelee/vllm focusing on stability, memory management, and CPU-only deployment improvements. Delivered OOM handling enhancements for version 1 initialization with improved error messaging and memory availability tests for key-value cache ops. Implemented Triton ScaledMM kernel fallback and enhanced kernel selection to improve compatibility in CPU-only environments, accompanied by configuration-compatibility tests.

November 2025

1 Commits • 1 Features

Nov 1, 2025

November 2025 monthly summary highlighting the delivery of a Map Functions Suite in DuckDB within the pg_duckdb integration, along with solid tests and collaboration. The work enhances data handling for map types and broadens SQL querying capabilities for analysts and applications relying on nested/map data structures.

October 2025

1 Commits • 1 Features

Oct 1, 2025

October 2025 (2025-10) — jeejeelee/vllm: Delivered a targeted inference performance optimization for fused_moe on Qwen3-30B running on H100 GPUs, with FP8 and BF16 data paths. The work tuned Triton kernel configurations and parameters to improve throughput and latency, enabling more efficient large-model inference on enterprise workloads. The changes establish a foundation for broader GPU-accelerated deployments and future optimizations across similar model/hardware combinations.

Activity

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Quality Metrics

Correctness90.0%
Maintainability80.0%
Architecture85.0%
Performance85.0%
AI Usage30.0%

Skills & Technologies

Programming Languages

CC++PythonSQL

Technical Skills

C programmingGPU ComputingKernel DevelopmentKernel TuningLarge Language ModelsMachine LearningPerformance OptimizationQuantizationSQLTestingTritonbackend developmentdatabase developmenterror handlingtesting

Repositories Contributed To

2 repos

Overview of all repositories you've contributed to across your timeline

jeejeelee/vllm

Oct 2025 Dec 2025
2 Months active

Languages Used

C++Python

Technical Skills

GPU ComputingKernel TuningLarge Language ModelsPerformance OptimizationTritonKernel Development

duckdb/pg_duckdb

Nov 2025 Nov 2025
1 Month active

Languages Used

CSQL

Technical Skills

C programmingSQLdatabase development