Running Low-Latency Analytical Workloads with GPU-Accelerated Presto on NVIDIA GB200 NVL72

Summary

GPU-accelerated Presto integrates NVIDIA cuDF algorithms and NVLink to deliver low-latency analytical query execution on massive datasets. Benchmarks derived from TPC-H across scale factors 1K and 3K reveal that a single NVIDIA DGX B200 system with eight GPUs outperforms 8- to 10-node Intel Xeon clusters by up to 8.2x. Scaling out across multinode NVIDIA GB200 NVL72 systems paired with IBM Storage Scale achieved additional performance gains. Enabling NVIDIA GPUDirect Storage, tuning I/O parameters, adjusting thread counts, and applying query rebatching delivered a cumulative 64% reduction in query runtimes. Topology-aware GPUDirect Storage cold reads proved roughly twice as fast as standard POSIX reads by avoiding CPU memory staging and NUMA boundary crossing penalties.

Takeaways

1. 1

   A single DGX B200 running eight GPUs delivered 8.2x faster query runtimes at scale factor 1K than an eight-node Intel Xeon 6642Y cluster.

2. 2

   NVIDIA GPUDirect Storage cold reads achieved roughly 2x faster runtimes than POSIX reads on GB200 NVL72 by bypassing host memory bounce buffers and avoiding NUMA boundary crossing.

3. 3

   I/O optimizations, 16 I/O threads, rebatching, and rewriting query Q11 to an INSERT INTO statement yielded an overall 64% query runtime reduction across 32 GPU workers on GB200 NVL72.