Phase 3A technical baseline POPULATED
Engineering overview
GPUs trade single-thread latency optimization for massive throughput. Many lightweight threads are scheduled across streaming multiprocessors, and workloads perform best when enough independent work exists to hide latency and when memory accesses are organized efficiently.
In NVIDIA’s SIMT model, threads are grouped into warps. Threads retain their own state and may branch independently, but divergent control flow and scattered memory access can reduce utilization. This makes algorithm structure and data layout as important as arithmetic count.
Core concepts
SIMT executionThreads execute in groups while preserving per-thread state and control-flow semantics.
Occupancy and latency hidingMultiple ready warps allow the scheduler to issue other work while one warp waits.
Memory hierarchyRegisters, shared/local resources, caches and global/HBM memory serve different bandwidth/latency roles.
CoalescingNearby threads accessing nearby addresses can reduce the number of memory transactions required.
Engineering workflow
- Confirm the problem exposes enough data/task parallelism to justify GPU acceleration.
- Map work into thread/block structures while minimizing synchronization and branch divergence.
- Stage reused data in appropriate on-chip memory and organize global accesses for locality/coalescing.
- Measure whether kernels are compute-bound, memory-bound or limited by occupancy/synchronization.
- Include host-device transfer, launch overhead and multi-GPU interconnect in end-to-end performance analysis.
Tradeoffs & failure modes
- Porting scalar CPU code without changing the data-parallel structure.
- Ignoring memory traffic while optimizing arithmetic.
- Excessive divergence within warps.
- Benchmarking kernels without including transfer and orchestration overhead.