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Accelerating Computational Workloads: GPU Architectures, Programming Models, and Applications
Comprehensive survey on GPU-based parallel computing, exploring GPU architectures, programming models, and their applications in networking, AI, and distributed systems.
GPU ComputingCUDAOpenCLParallel ComputingDistributed SystemsHigh-Performance Computing
About the Project
Co-authored a comprehensive survey paper examining the evolution of GPU architectures from graphics rendering to general-purpose computing. The paper investigates GPU programming models (CUDA, OpenCL), their applications in networking systems, distributed machine learning, edge computing, and high-performance computing. Analyzes performance optimizations, energy efficiency considerations, and future trends in GPU-accelerated computing.