Collaborative Research: Dynamic Staging Architecture for Accelerating I/O Pipelines

Petascale applications are producing terabytes of data at a great rate. Storage systems in large-scale machines are significantly stressed as I/O rates are not growing as fast to cope with data production. A variety of HPC activities such as writing output and checkpoint data are all stymied by the I/O bandwidth bottleneck. Further to this, the post-processing and subsequent analysis/visualization of computational results is increasingly time consuming due to the widening gap between the storage/processing capacities of supercomputers and users' local clusters.

This research focuses on building a novel in-job dynamic data staging architecture and in bringing it to bear on the looming petascale I/O crisis. To this end, the following objectives are investigated: (i) the concerted use of node-local memory and emerging hardware such as Solid State Disks (SSDs), from a dedicated set of nodes, as a means to alleviate the I/O bandwidth bottleneck, (ii) the multiplexing of traditional user post-processing pipelines and secondary computations with asynchronous I/O on the staging ground to perform scalable I/O and data analytics, (iii) bypassing memory to access the staging area, and (iv) enabling QoS both in the staging ground and in the communication channel connecting it to compute client and persistent storage.

This study will have a wide-ranging impact on future provisioning of extreme-scale machines and will provide formative guidelines to this end. The result of this research will be a set of integrated techniques that can fundamentally change the current parallel I/O model and accelerate petascale I/O pipelines. Further, this research will help analyze the utility of SSDs in day-to-day supercomputing I/O and inform the wider HPC community of its viability.