Memory Layout Optimization in PyTorch CompilationVisualization of memory layout optimization techniques in PyTorch kernel fusion. The diagram shows how standard execution leads to memory access inefficiency while optimized layouts improve data locality and reduce memory latency.Memory Optimization via Kernel Fusion (e.g., torch.compile)Before Compilation: Eager Execution (Separate Kernels)GPU Memory HierarchyGlobal Memory (High Capacity, High Latency)L2 CacheL1 Cache / Shared MemoryRegisters (Fastest, Lowest Latency)Kernel 1 (Linear)Kernel 2 (ReLU)Kernel 3 (BatchNorm)Kernel 4 (Conv)Load InputStore Interm. ALoad Interm. AStore Interm. BLoad Interm. BStore Interm. CLoad Interm. CStore OutputBottleneck: Frequent Global Memory reads/writes for intermediate results.Bottleneck: High overhead from launching many small kernels.After Compilation: Optimized Execution (Fused Kernels)GPU Memory HierarchyGlobal MemoryL2 CacheL1 Cache / Shared MemoryRegistersFused Kernel A(Linear)(ReLU)Fused Kernel B(BatchNorm)(Conv)Load InputInterm. A'(in Regs/L1)Store Interm. XLoad Interm. XInterm. B'(in Regs/L1)Store OutputBenefit: Intermediate results (A', B') kept in fast memory, avoiding Global Memory writes/reads.Benefit: Fewer kernel launches (2 instead of 4), reducing CPU-GPU synchronization overhead.
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