No abstract

Both distributed systems and multicore systems are difficult programming environments. Although the expert programmer may be able to carefully tune these systems to achieve high performance, the non-expert may struggle. We argue that high level abstractions are an effective way of making parallel computing accessible to the non-expert. An abstraction is a regularly structured framework into which a user may plug in simple sequential programs to create very large parallel programs. By virtue of a regular structure and declarative specification, abstractions may be materialized on distributed, multicore, and distributed multicore systems with robust performance across a wide range of problem sizes. In previous work, we presented the All-Pairs abstraction for computing on distributed systems of single CPUs. In this paper, we extend All-Pairs to multicore systems, and introduce the Wavefront and Makeflow abstractions, which represent a number of problems in economics and bioinformatics. We demonstrate good scaling of both abstractions up to 32 cores on one machine and hundreds of cores in a distributed system.

Data-intensive applications involving the analysis of large datasets often require large amounts of compute and storage resources, for which data locality can be crucial to high throughput and performance. We propose a "data diffusion" approach that acquires compute and storage resources dynamically, replicates data in response to demand, and schedules computations close to data. As demand increases, more resources are acquired, thus allowing faster response to subsequent requests that refer to the same data; when demand drops, resources are released. This approach can provide the benefits of dedicated hardware without the associated high costs, depending on workload and resource characteristics. To explore the feasibility of data diffusion, we offer both a theoretical and an empirical analysis. We define an abstract model for data diffusion, introduce new scheduling policies with heuristics to optimize real-world performance, and develop a competitive online cache eviction policy. We also offer many empirical experiments to explore the benefits of dynamically expanding and contracting resources based on load, to improve system responsiveness while keeping wasted resources small. We show performance improvements of one to two orders of magnitude across three diverse workloads when compared to the performance of parallel file systems with throughputs approaching 80 Gb/s on a modest cluster of 200 processors. We also compare data diffusion with a best model for active storage, contrasting the difference between a pull-model found in data diffusion and a push-model found in active storage.

Abstract-Today, campus grids provide users with easy access to thousands of CPUs. However, it is not always easy for nonexpert users to harness these systems effectively. A large workload composed in what seems to be the obvious way by a naive user may accidentally abuse shared resources and achieve very poor performance. To address this problem, we argue that campus grids should provide end users with high-level abstractions that allow for the easy expression and efficient execution of data intensive workloads. We present one example of an abstraction -All-Pairs -that fits the needs of several applications in biometrics, bioinformatics, and data mining. We demonstrate that an optimized All-Pairs abstraction is both easier to use than the underlying system, achieves performance orders of magnitude better than the obvious but naive approach, and is both faster and more efficient than a tuned conventional approach. This abstraction has been in production use for one year on a 500-CPU campus grid at the University of Notre Dame, and has been used to carry out a groundbreaking analysis of biometric data.

Abstract. Traditional distributed filesystem technologies designed for local and campus area networks do not adapt well to wide area grid computing environments. To address this problem, we have designed the Chirp distributed filesystem, which is designed from the ground up to meet the needs of grid computing. Chirp is easily deployed without special privileges, provides strong and flexible security mechanisms, tunable consistency semantics, and clustering to increase capacity and throughput. We demonstrate that many of these features also provide order-of-magnitude performance increases over wide area networks. We describe three applications in bioinformatics, biometrics, and gamma ray physics that each employ Chirp to attack large scale data intensive problems.