Tamer Mahmoud

Enterprise leader, ICT Technologist and industry expert, leading, managing, and performing research, development, and implementation of technology challenging systems, in a wide range of domains over 25 years.
Since 2008, Tamer has been a pioneer and an influential, focusing on parallel processing adoption and efficient computing, registered IPs focusing on his own Real-Time A/Video Streaming and Processing engines applying parallel and accelerated computing.
Early since 2012, Tamer has predicted the generalization of HPC beyond research labs and established the “HPC Life” strategy, applied to HPC-Query project, Video Processing, RT Graphs, GPU DB, and “MyClass” engagement measurement tools for remote education, Distributed Video Analytics Platform upon COVID 2019.
In 2020, he authored the “Optimization Strategy for Compute Intensive systems”, and applied it to the world’s fastest CFD implementation, achieving more than 1500x speedup.
Recently focusing on the inception of Data-Centric computing, and AI over HPC systems architecture, identifying efficient technology alternatives in HPC systems marking in the TOP500, and emphasizing cross-platform integration, data access and transfer efficiencies.
Tamer is currently researching and developing methods and tools for future software development, focusing on performance, large code-base management, and unified models targeting multiplatform, among other objectives.

Specialties:
• High Performance Computing HPC

• Leading Multi-vendor Mega projects, covering business and technology aspects
• Public speaking, Course authoring, and Lecturing
• Innovative solutions and platform architecture, integrating multiple technologies

• Parallel Computing, multi-core, multi-node
• GPGPU, and accelerators
• Processing & Streaming Video, Audio, Image, and Computer Vision
• Cache, memory, and system internal data transfer
• HPC Networking 
• HPC Data Storage, Access, Transfer, and Retrieval
• Optimization of, system, network, process, algorithm, memory
• HW (CPU, GPU, APU) specific acceleration and Low-Level optimization, SIMD, SIMT, AVX, AMX, XMX, ...