Summary
Damodar Sahasrabudhe is a Member of Technical Staff based in Sunnyvale with a decade of experience building high-performance scientific and ML systems for supercomputing and accelerator hardware. He holds a PhD from the University of Utah and has driven performance-portability work—introducing Kokkos, portable SIMD primitives, and an asynchronous heterogeneous task scheduler—that enabled scaling Uintah to 1024 GPUs and delivered multi-fold speedups on KNL, V100 and P100 platforms. At Cerebras he now develops ML/deep learning kernels tuned for wafer-scale accelerators, bringing research-grade optimizations into production hardware. Earlier roles at Sandia, Deloitte and Wipro combine deep HPC expertise with hands-on systems integration, MDM and multilingual enterprise application delivery. He also built a fault-tolerance component for supercomputing that recovered from node failures 10x faster than traditional checkpoint/restart, illustrating a pragmatic focus on resilience as well as raw performance. Practical, research-driven and delivery-oriented, he bridges PhD-level HPC research with production software at scale.
10 years of coding experience
14 years of employment as a software developer
The University of Utah
Bachelor of Engineering (B.E.), Computer Science, First Class with distinction, Bachelor of Engineering (B.E.), Computer Science, First Class with distinction at Maharashtra Institute of Technology
English, Hindi, Marathi, Telugu