Parallel I/O plays a crucial role for most data-intensive applications running on massively parallel systems like Blue Gene/L that provides the promise of delivering enormous computational capability. We designed and implemented a highly scalable parallel file I/O architecture for Blue Gene/L, which leverages the benefit of the hierarchical and functional partitioning design of the system software with separate computational and I/O cores. The architecture exploits the scalability aspect of GPFS (General Parallel File System) at the backend, while using MPI I/O as an interface between the application I/O and the file system. We demonstrate the impact of our high performance I/O solution for Blue Gene/L with a comprehensive evaluation that consists of a number of widely used parallel I/O benchmarks and I/O intensive applications. Our design and implementation is not only able to deliver at least one order of magnitude speed up in terms of I/O bandwidth for a real-scale application HOMME (achieving aggregate bandwidth of 1.8 GB/Sec and 2.3 GB/Sec for write and read accesses, respectively), but also supports high-level parallel I/O data interfaces such as parallel HDF5 and parallel NetCDF scaling up to a large number of processors.

Blue Gene/L is currently the world's fastest and most scalable supercomputer. It has demonstrated essentially linear scaling all the way to 131,072 processors in several benchmarks and real applications. The operating systems for the compute and I/O nodes of Blue Gene/L, are among the components responsible for that scalability. Compute nodes are dedicated to running application processes, whereas I/O nodes are dedicated to performing system functions. The operating systems adopted for each of these nodes reflect this separation of function. Compute nodes run a lightweight operating system called the compute node kernel. I/O nodes run a port of the Linux operating system. This paper discusses the architecture and design of this solution for Blue Gene/L in the context of the hardware characteristics that led to the design decisions. It also explains and demonstrates how those decisions are instrumental in achieving the performance and scalability for which Blue Gene/L is famous.