{"href":"https://api.simplecast.com/oembed?url=https%3A%2F%2Fshared-everything.simplecast.com%2Fepisodes%2Fhow-cineca-is-rebuilding-hpc-for-ai-sAUBwr3j","width":444,"version":"1.0","type":"rich","title":"How CINECA Is Rebuilding HPC for AI","thumbnail_width":300,"thumbnail_url":"https://image.simplecastcdn.com/images/ffdb3e35-ef24-4933-8ca3-b2448234e81f/b597f6ee-b37b-4aa7-a060-ea8f97cc9431/set-dark-stacked.jpg","thumbnail_height":300,"provider_url":"https://simplecast.com","provider_name":"Simplecast","html":"<iframe src=\"https://player.simplecast.com/5ad0df77-e1ca-4a57-aba4-22c34d3eca8e\" height=\"200\" width=\"100%\" title=\"How CINECA Is Rebuilding HPC for AI\" frameborder=\"0\" scrolling=\"no\"></iframe>","height":200,"description":"AI is changing the I/O profile of supercomputing, and CINECA is seeing that shift directly on Leonardo, where AI now accounts for more than half of the workload. Daniele Cesarini, Head of AI/HPC Architecture at CINECA, explains why AI training and inference introduce data-access patterns that differ sharply from traditional simulation, putting new pressure on parallel file systems, metadata performance, small-file access and GPU data pipelines. The conversation gets into CINECA’s move toward a more flexible data architecture capable of serving both HPC and AI, the role of object and multiple access protocols alongside conventional HPC storage, and how lessons from Leonardo are informing the architecture of CINECA’s next AI infrastructure, Italia, where keeping more than 8,000 GPUs efficiently fed becomes a data problem as much as a compute problem."}