Mark Megerian

Blue Gene/L programming and operating environment

With up to 65,536 compute nodes and a peak performance of more than 360 teraflops, the Blue Gene®/L (BG/L) supercomputer represents a new level of massively parallel systems. The system software stack for BG/L creates a programming and operating environment that harnesses the raw power of this architecture with great effectiveness. The design and implementation of this environment followed three major principles: simplicity, performance, and familiarity. By specializing the services provided by each component of the system architecture, we were able to keep each one simple and leverage the BG/L hardware features to deliver high performance to applications. We also implemented standard programming interfaces and programming languages that greatly simplified the job of porting applications to BG/L. The effectiveness of our approach has been demonstrated by the operational success of several prototype and production machines, which have already been scaled to 16,384 nodes.

Blue Gene/Q resource management architecture

As supercomputers scale to a million processor cores and beyond, the underlying resource management architecture needs to provide a flexible mechanism to manage the wide variety of workloads executing on the machine. In this paper we describe the novel approach of the Blue Gene/Q (BG/Q) supercomputer in addressing these workload requirements by providing resource management services that support both the high performance computing (HPC) and high-throughput computing (HTC) paradigms. We explore how the resource management implementations of the prior generation Blue Gene (BG/L and BG/P) systems evolved and led us down the path to developing services on BG/Q that focus on scalability, flexibility and efficiency. Also provided is an overview of the main components comprising the BG/Q resource management architecture and how they interact with one another. Introduced in this paper are BG/Q concepts for partitioning I/O and compute resources to provide I/O resiliency while at the same time providing for faster block (partition) boot times. New features, such as the ability to run a mix of HTC and HPC workloads on the same block are explained, and the advantages of this type of environment are examined. Similar to how Many-task computing (MTC) [1] aims to combine elements of HTC and HPC, the focus of BG/Q has been to unify the two models in a flexible manner where hybrid workloads having both HTC and HPC characteristics are managed simultaneously.

IBM Blue Gene/Q system software stack

The principal focus areas for system software on the IBM Blue Gene®/Q include ultrascalability and high reliability while delivering the full performance capability of the hardware to applications. The Blue Gene/Q system software has achieved these goals while adding functionality and flexibility compared with previous versions of Blue Gene®. Whereas part of the software stack was improved with innovative evolutionary progress, such as unified sub-block partitioning and the ability to overcommit hardware threads, other areas, such as transactional memory and speculative execution, represent a revolutionary step forward. In this paper, we describe the overall software architecture of Blue Gene/Q. We then describe each of the main components of the software stack. In each area, we focus on the major enhancements introduced in the Blue Gene/Q system software stack.

A Flexible Resource Management Architecture for the Blue Gene/P Supercomputer

Blue Genereg/P are a massively parallel supercomputer intended as the successor to Blue Gene/L. It leverages much of the existing architecture of its predecessor to provide scalability up to a petaflop of peak computing power. The resource management software for such a large parallel system faces several challenges, including system fragmentation due to partitioning, presenting resource usage information using a polling or event driven model, and acting as a barrier between external resource management systems and the Blue Gene/P core. This paper describes how the Blue Gene/P resource management architecture is extremely flexible by providing multiple methodologies for obtaining resource usage information to make scheduling decisions. Three distinctly separate resource management services can be described. First, the Bridge API, a full-featured API suitable for fine tuning scheduling and allocation decisions. Second, a light-weight allocator API for allocating resources without substantial development costs. And lastly, configuring the system into static partitions. Job scheduling strategies utilizing each of the methods can be discussed.