Mukund Waghmare
Software Engineer (Hpc) @Cdacindia
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WORK HISTORY
Software Engineer (Hpc) @Cdacindia
Noida, IN
Parallel Performance Optimization of Quantum Circuit Simulation on Multi-FPGA HPC Systems• Optimized quantum circuit simulation on FPGA accelerators by removing sequential execution and enabling parallel circuit partitioning across multiple FPGAs.• Improved performance and scalability through host–FPGA orchestration redesign, kernel scheduling optimization, and concurrency control, applying HPC parallelization principles.• Performed end-to-end performance analysis across Python host layers and FPGA kernels to identify bottlenecks and synchronization overheads.AI-Driven Soil Nutrition Analysis System• Built a hybrid rule-based and ML-driven system for soil nutrient analysis using Python-based data pipelines.• Designed workflows with scalability in mind, suitable for execution on larger compute or HPC-enabled analytics environments.Development of Application Performance Analysis Tools for Energy Profiling and Power Cap Techniques in HPC Environments • Designed and developed performance analysis tools for energy profiling in HPC environments. • Conducted power consumption analysis in CPU power management states to optimize system performance. • Gained hands-on experience with the Param Utkarsh Super Computer (838 TFLOPS RMAX) for highefficiency, real-time computations. • Applied parallel programming techniques and scientific computing to enhance high computation deployment, focusing on reliability and throughput.
EDUCATION
CDAC ACTS Pune
PGD - HPC & AI Application Programming
Savitribai Phule Pune University
M. Sc. , Electronics and Instrumentation Science
Swami Ramanand Teerth Marathwada University
Bachelor's degree, Electronics Along with Physics and Mathematics
ABOUT MUKUND WAGHMARE
I am an HPC Engineer specializing in the design, operation, and optimization of SLURM-managed CPU and GPU clusters. My work focuses on building reliable high-performance computing environments, improving workload efficiency, and ensuring performance stability across large-scale scientific and AI applications. I have hands-on experience in Linux-based HPC infrastructure, cluster configuration, job scheduling policies, and multi-node workload management using MPI and OpenMP. I work extensively with GPU acceleration (CUDA) and apply performance profiling and optimization techniques to improve throughput, scalability, and resource utilization. My recent work includes developing power-aware performance optimization mechanisms for HPC environments, where I achieved measurable improvements in energy efficiency while maintaining stable execution performance. I have also worked on multi-FPGA quantum workload parallelization, achieving significant speedup through accelerator-level orchestration and concurrency optimization. Beyond implementation, I actively contribute to HPC knowledge development through mentoring and training in Linux, parallel programming, and GPU computing, aligned with large-scale national supercomputing initiatives. Core Areas of Interest: • HPC Cluster Operations & Administration • SLURM Workload Management • Performance & Energy Optimization • Parallel & Distributed Computing (MPI/OpenMP) • GPU & Accelerator Computing • Scalable AI-HPC Integration I am always interested in collaborating on large-scale computing challenges involving performance engineering, infrastructure optimization, and next-generation HPC systems.
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