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⚡ CUDA

🔓 OpenCL

🔀 Parallel

🧵 Warp

🖥️ GPU Computing

CUDA • OpenCL • Parallel Programming • GPU Architecture • HPC • Deep Learning

Master the art of massively parallel processing — from GPU architecture and CUDA programming to high-performance computing, deep learning acceleration, and scientific simulation.

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GPU Computing Concepts & Technologies

GPU Computing Basics

CPU vs GPU, SIMD/SIMT, parallel processing.

GPU Architecture

Streaming processors, memory hierarchy, cores.

Parallel Computing Concepts

Amdahl's law, speedup, scalability, overhead.

CUDA Programming Basics

Kernel launch, threads, blocks, grids.

CUDA Memory Management

Global, shared, local, constant, unified memory.

CUDA Optimizations

Memory coalescing, bank conflicts, occupancy.

CUDA Shared Memory

Shared memory, tiling, reduction, synchronization.

CUDA Dynamic Parallelism

Nested kernels, recursive patterns.

OpenCL Basics

Platform, devices, context, command queue.

OpenCL Memory Model

Global, constant, local, private memory.

OpenCL Optimizations

Work-group size, coalescing, memory locality.

OpenCL Devices

CPUs, GPUs, FPGAs, DSPs, heterogeneous.

GPU Memory Hierarchy

Registers, L1/L2 cache, HBM, GDDR.

GPU Cache Optimization

Texture cache, constant cache, L2.

Parallel Reduction

Sum, min/max, prefix sums, tree reduction.

Parallel Prefix (Scan)

Work-efficient, inclusive/exclusive, Brent-Kung.

Parallel Sorting

Bitonic sort, radix sort, sample sort.

Matrix Multiplication

Naive, tiled, shared memory, tensor cores.

Deep Learning on GPU

CUDA-based, cuDNN, TensorRT, PyTorch.

Tensor Cores

Mixed precision, matrix multiply-accumulate.

Mixed Precision Training

FP16/FP32, loss scaling, faster training.

CUDA Libraries

cuBLAS, cuFFT, cuDNN, Thrust.

NVIDIA RAPIDS

GPU-accelerated data science, cuDF, cuML.

OpenCL Libraries

clBLAS, clFFT, clSparse.

HPC with GPUs

Supercomputing, MPI+CUDA, cluster computing.

Multi-GPU Programming

Peer-to-peer, NCCL, distributed training.

GPU Cluster Computing

Distributed systems, cloud GPUs, orchestration.

GPU Rendering Pipeline

Vertex, geometry, rasterization, fragment.

Ray Tracing on GPU

BVH, RT cores, real-time ray tracing.

Scientific Computing

Physics simulations, weather, CFD, biology.

N-Body Simulation

Gravity, Barnes-Hut, particle-particle.

Molecular Dynamics

AMBER, GROMACS, GPU acceleration.

GPU Profiling

NVIDIA Nsight, Visual Profiler, timings.

GPU Debugging

cuda-gdb, race conditions, memory errors.

ROCm (AMD)

HIP, ROCm stack, AMD GPUs.

oneAPI

Unified programming model, DPC++.

Quantum Computing on GPU

Quantum simulation, Qiskit, tensor networks.

Edge GPU Computing

NVIDIA Jetson, TensorRT, edge AI.

Embedded GPU Computing

ARM+GPU, low-power, mobile GPUs.

Cloud GPU Computing

AWS, GCP, Azure, cloud GPUs.

GPU Virtualization

vGPU, SR-IOV, container GPUs.

🖥️ GPU Computing Mastery Roadmap

🔰 Foundation

GPU architecture, parallel concepts, CUDA/OpenCL basics

📶 Core

Kernel programming, memory management, parallel algorithms

🔥 Deep Dive

Optimization, tensor cores, multi-GPU, deep learning

🏆 Frontier

Distributed HPC, quantum, ROCm, oneAPI

🖥️ Accelerate Performance — Master GPU Computing

Hands-on labs, performance optimization challenges, and real-world HPC applications.

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