GPU Streaming Multiprocessor (SM) Architecture is the fundamental execution unit of GPU chips, containing dozens of CUDA cores, tensor cores, warp schedulers, and hierarchical cache/memory subsystems orchestrated to achieve massive thread parallelism and memory bandwidth.

CUDA Core and Tensor Core Organization

• CUDA Cores: Scalar processing elements executing FP32 (single-precision) or integer operations. Typical SM: 32-128 CUDA cores. Each core contains FP unit, integer ALU, and special function unit (SFU).
• Tensor Cores: Specialized units performing matrix multiplication (4×4 or 8×8 matrix ops in few cycles). Recent GPUs (Volta+) dedicate substantial area to tensor cores (10-20 cores per SM).
• Special Function Units (SFU): Execute transcendental functions (sin, cos, reciprocal), integer operations. Typically 1 SFU per warp (32 threads) limiting throughput for special functions.

Warp Scheduling Hardware

• Warp Concept: Group of 32 threads executing in lockstep (SIMD). Modern GPUs issue 2-4 warps per cycle, each to different execution units.
• Warp Scheduler: Selects ready warps (no stalls) for execution from resident warps (typically 32-64 per SM). Scheduling policies: round-robin, priority-based, or two-level hierarchical.
• Ready Warp Identification: Tracks register availability, operand readiness, instruction fetch completion. Warp marked "stalled" when waiting for memory, synchronization, or resources.
• Dual-Issue Architecture: Modern designs issue two independent instructions from same warp or different warps. Enables pipelining and hiding latencies.

Register File Banking and Architecture

• Register File Size: 64-256 KB per SM (Ampere: 256 KB). Distributed as 32 banks, one read port per bank per cycle.
• Bank Conflict: Simultaneous accesses to same register bank by different threads. Causes serialization (pipeline stall) limiting throughput.
• Banking Layout: Registers allocated sequentially to threads. Thread i's registers in bank (i mod 32). Stride-1 accesses have no conflicts; stride-32 accesses fully serialize.
• Register Optimization: Compiler allocates registers to minimize bank conflicts. Unroll loops to increase register pressure but improve ILP. Register spilling to local memory expensive (~10x slower).

L1 Cache and Shared Memory Integration

• L1 Cache: 32-64 KB per SM. Caches all memory accesses (if enabled). Separate banks from shared memory in Ampere (flexible partitioning).
• Shared Memory: 48-96 KB fast on-chip memory, explicitly managed by programmer. Bank-conflict free access with properly aligned patterns (sequential access best).
• Write-Through Behavior: L1 write-through to L2 (no write-back buffering in early GPU architectures). Recent designs: write-back option for reduced memory traffic.

Load-Store Unit and Memory Subsystem

• Load-Store Capability: SM can issue multiple load/store instructions per cycle. Coalesced accesses (consecutive threads accessing consecutive memory addresses) merge into single bus transaction.
• Coalescing Efficiency: 32 consecutive loads (4-byte words) coalesce into one 128-byte transaction. Scattered patterns waste bandwidth.
• Memory Latency Hiding: 100-500 cycle memory latency hidden by scheduling other ready warps. Occupancy (resident warp count) determines latency hiding capability.

Occupancy and Latency Hiding

• Occupancy Metric: Percentage of maximum resident warps actually resident. Higher occupancy better hides memory latency (more warps available to schedule while others wait).
• Limiting Factors: Register pressure, shared memory allocation per thread, block size constraints determine max occupancy (typically 50-100%).
• Ampere/Hopper Evolution: Larger register files (256 KB), flexible shared memory partitioning, tensor float 32 (TF32) precision enable higher occupancy while maintaining performance.