heterogeneous

**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.