Heterogeneous Computing CPU GPU FPGA is a computational paradigm leveraging diverse processing elements with different strengths, matching tasks to optimal processing units — Heterogeneous computing exploits the complementary strengths of different processors: CPUs excel at complex control, GPUs at massive parallelism, and FPGAs at customized computation. CPU Characteristics provide sophisticated control flow, branch prediction, large caches, strong scalar performance, ideal for irregular algorithms and control-intensive tasks. GPU Strengths deliver massive parallel throughput through thousands of cores, high memory bandwidth, energy efficiency on data-parallel workloads, optimal for dense matrix operations. FPGA Advantages enable custom datapaths, ultra-low-latency operation, specialized arithmetic, efficient for streaming workloads and niche algorithms. Task Mapping assigns different computation phases to optimal processors, CPU handling setup and data marshaling, GPU computing bulk operations, FPGA processing specialized kernels. Data Movement minimizes transfers between processors through careful data partitioning, batching operations to amortize transfer overhead. Programming Models abstract hardware details enabling portable code across heterogeneous systems through OpenCL, CUDA, HIP runtime APIs. Load Balancing distributes work across heterogeneous resources accounting for different compute capabilities, prevents bottlenecks from slowest processors. Heterogeneous Computing CPU GPU FPGA delivers application performance through processor specialization.