Raiyan Latif
Software Engineer at Intel Corporation
Portland, Oregon, United States
Summary
🤩
Rockstar🎓
Top SchoolRaiyan Latif is a software engineer with a decade of experience building low-level Linux software for Intel, currently focused on GPU runtime and driver development in Portland, Oregon. His work spans kernel and library development for high-performance compute and fabric products, with a strong track record of optimizing memory management, P2P transfers, and blitter operations to improve kernel performance. He brings hands-on expertise in C/C++ and Linux kernel internals from both industry roles and graduate research optimizing mobile storage stacks. An active contributor to Intel’s compute-runtime, Raiyan has implemented subtle, performance-critical enhancements such as indirect allocation access and kernel memory attribute helpers. Colleagues rely on him for pragmatic, systems-level solutions that bridge research insight and production-quality code.
10 years of coding experience
5 years of employment as a software developer
Bachelor of Science (B.S.), Electrical & Computer Engineering, Bachelor of Science (B.S.), Electrical & Computer Engineering at Oregon State UniversityMaster of Science (M.S.), Computer Engineering, Master of Science (M.S.), Computer Engineering at Purdue University
Github Skills (7)
memory-management10
c-language10
cprogramming-language10
gpgpu9
gpu9
computation9
opencl9
Programming languages (3)
Github contributions (5)
intel/compute-runtime
Jun 2019 - Jan 2023
Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 commits in 3 years 7 months
Contributions summary:Raiyan primarily focused on modifying and integrating core functionalities related to memory management and access within the Intel compute runtime. They introduced and implemented helper functions for checking kernel memory attributes and enabling support for indirect allocation access. Further contributions included addressing issues related to P2P memory copies and blitter operations, ensuring efficient data movement, and fixing related errors, indicating a focus on low-level optimization and memory handling. These efforts seem to be geared towards improving kernel performance.
zerogpuruntimeinteloneapi
raiyanla/unified-runtime
Dec 2023 - Aug 2024
Contributions:92 pushes, 13 branches in 7 months
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