Heterogeneous Computing

Heterogeneous Computing — using multiple types of processors (CPU, GPU, FPGA, custom accelerators) within a single system, assigning each workload to the processor best suited for it.

Why Heterogeneous?

• No single processor is optimal for all workloads
• CPU: Great for sequential, branch-heavy code. Latency-optimized
• GPU: Great for massively parallel, data-parallel work. Throughput-optimized
• FPGA: Great for custom dataflow, low-latency, bit-manipulation
• Custom ASIC: Maximum efficiency for specific fixed algorithms

Common Heterogeneous Architectures

• CPU + GPU: Most common. Used in AI training/inference, HPC, graphics
• CPU + FPGA: Network processing (SmartNICs), low-latency trading, genomics
• CPU + AI Accelerator: Google TPU, Apple Neural Engine, Intel Gaudi
• SoC: Mobile chips integrate CPU + GPU + NPU + ISP + DSP (Apple M-series, Qualcomm Snapdragon)

Programming Models

• CUDA: NVIDIA GPU programming (dominant for AI/HPC)
• OpenCL: Cross-vendor GPU/FPGA/CPU programming (portable but less optimized)
• SYCL/oneAPI: Intel's cross-architecture programming model
• ROCm/HIP: AMD GPU programming (CUDA-compatible API)
• Vitis/Vivado HLS: FPGA programming with C++ synthesis

Challenges

• Data movement: Transferring data between CPU and accelerator is expensive
• Programming complexity: Different programming models for each device
• Load balancing: Partitioning work optimally across different processors
• Portability: Code written for one accelerator may not run on another

Heterogeneous computing defines the future of computing — as Moore's Law slows, specialized accelerators are the primary path to continued performance improvement.