Analysis of shared memory between CPUs and GPUs

Abstract

Heterogeneous architectures featuring CPUs and GPUs in one system are increasingly adopted for high-performance data processing, yet interconnect bandwidth and memory capacity remain primary bottlenecks on the GPU side. While high-end solutions like NVIDIA Grace Hopper mitigate these issues via specialized interconnects, their high cost limits widespread adoption. We investigate the memory capabilities of the NVIDIA DGX Spark, a novel platform featuring a unified memory architecture where DDR memory is located on the CPU and is fully accessible from the GPU. We analyze the performance and tuning of such a system and explore how data processing workloads can be best run on such shared memory architectures. The demonstration will showcase how memory is allocated, the performance implications of different configurations, the results of running a data analytics benchmark, and the tools used to run the benchmarks, insert instrumentation, and analyze the results.