Sathish Kumar M

Generative AI models, such as Stable Diffusion, have revolutionized the field of AI by enabling the generation of images from textual prompts. These models impose significant computational, and storage demands in HPC environments. The I/O workload generated during image generation is a critical factor affecting overall performance and scalability. This paper presents a detailed analysis of the I/O workload generated by Stable Diffusion when accessing storage devices, specifically NVMe-oF drives. The study explores various I/O patterns, including read and write operations, latency, throughput, bandwidth, LBA mappings, WAF (Write Amplification Factor) influence the performance of generative AI workloads. The IO pattern shows that how it affects the WAF of the SSD when multiple user requests. By using containerized Stable Diffusion deployed on FDP(Flexible Data Placement) enabled environment as a case study, we investigate how different storage configurations affects the efficiency of image generation and reduce the WAF for individual and concurrent user requests. We have developed a tool that provides insights into I/O activity on storage devices. It provides the graphical view of logical block address (LBA) mapping of I/O hits, block size and a granular view of data access patterns. This enables in-depth I/O analysis, helps identify performance bottlenecks, uncovers latency patterns, and supports optimization across the hardware and software stack.

Sathish Kumar M

Associate Technical Director

Samsung Semiconductor India Research