GPU Kernel Optimization

Anthropic's "GPU Kernel Optimization" interview question appears to be a performance engineering take-home challenge focused on optimizing a compute kernel, likely involving low-level code like CPU intrinsics or GPU programming (CUDA/PTX), rather than a traditional standalone problem with formal I/O specs.[7][9]

Problem Context

The challenge requires minimizing CPU cycles (or equivalent GPU execution time) for a kernel operation, such as data transposition or matrix computation on specialized hardware like TPUs or NVIDIA GPUs. It draws from real Anthropic engineering tasks, testing optimization skills in areas like vectorization (SIMD/AVX), memory access patterns, and register pressure reduction.[3][5]

Key Details Found

• Objective: Beat baseline performance metrics, e.g., under 1487 cycles on a reference implementation (Claude Opus 4.5 benchmark).[9]
• Implementation: Provided in perf_takehome.py, where participants modify a kernel for maximum efficiency while preserving correctness.[7]
• No full public problem statement, input/output examples, or constraints were located across searches—likely proprietary or recently open-sourced without complete disclosure.[1][5]

Optimization Focus Areas

Common techniques emphasized include:

• Shared memory optimization and bank conflict avoidance.
• Warp-level primitives and occupancy maximization.
• Profile-guided fixes for bottlenecks like register spilling.[1]