A trend is developing in high performance computing in which commodity processors are coupled to various types of computational accelerators. Such systems are commonly called hybrid systems. In this paper, we describe our experience developing an implementation of the Linpack benchmark for a petascale hybrid system, the LANL Roadrunner cluster built by IBM for Los Alamos National Laboratory. This system combines traditional x86-64 host processors with IBM PowerXCell™ 8i accelerator processors. The implementation of Linpack we developed was the first to achieve a performance result in excess of 1.0 PFLOPS, and made Roadrunner the #1 system on the Top500 list in June 2008. We describe the design and implementation of hybrid Linpack, including the special optimizations we developed for this hybrid architecture. We then present actual results for single node and multi-node executions. From this work, we conclude that it is possible to achieve high performance for certain applications on hybrid architectures when careful attention is given to efficient use of memory bandwidth, scheduling of data movement between the host and accelerator memories, and proper distribution of work between the host and accelerator processors.
Commercial database management systems (DBMSs) have historically seen very limited use within the scientific computing community. One reason for this absence is that previous database systems lacked support for the extensible data structures and performance features required within a high-performance computing context. However, database vendors have recently enhanced the functionality of their systems by adding object extensions to the relational engine. In principle, these extensions allow for the representation of a rich collection of scientific datatypes and common statistical operations. Utilizing these new extensions, this paper presents a study of the suitability of incorporating two popular scientific formats, NetCDF and HDF, into an object-relational system. To assess the performance of the database approach, a series of solution variables from a regional weather forecast model are used to build representative small, medium and large databases. Common statistical operations and array element queries are then performed using the object-relational database, and the execution timings are compared against native NetCDF and HDF operations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.