Abstract-Unmatched computation and storage performance in new HPC systems have led to a plethora of I/O optimizations ranging from application-side collective I/O to network and disk-level request scheduling on the file system side. As we deal with ever larger machines, the interference produced by multiple applications accessing a shared parallel file system in a concurrent manner becomes a major problem. Interference often breaks single-application I/O optimizations, dramatically degrading application I/O performance and, as a result, lowering machine wide efficiency.This paper focuses on CALCioM, a framework that aims to mitigate I/O interference through the dynamic selection of appropriate scheduling policies. CALCioM allows several applications running on a supercomputer to communicate and coordinate their I/O strategy in order to avoid interfering with one another. In this work, we examine four I/O strategies that can be accommodated in this framework: serializing, interrupting, interfering and coordinating. Experiments on Argonne's BG/P Surveyor machine and on several clusters of the French Grid'5000 show how CALCioM can be used to efficiently and transparently improve the scheduling strategy between two otherwise interfering applications, given specified metrics of machine wide efficiency.

With exascale computing on the horizon, the performance variability of I/O systems represents a key challenge in sustaining high performance. In many HPC applications, I/O is concurrently performed by all processes, which leads to I/O bursts. This causes resource contention and substantial variability of I/O performance, which significantly impacts the overall application performance and, most importantly, its predictability over time. In this paper, we propose a new approach to I/O, called Damaris, which leverages dedicated I/O cores on each multicore SMP node, along with the use of sharedmemory, to efficiently perform asynchronous data processing and I/O in order to hide this variability. We evaluate our approach on three different platforms including the Kraken Cray XT5 supercomputer (ranked 11th in Top500), with the CM1 atmospheric model, one of the target HPC applications for the Blue Waters postpetascale supercomputer project. By overlapping I/O with computation and by gathering data into large files while avoiding synchronization between cores, our solution brings several benefits: 1) it fully hides jitter as well as all I/O-related costs, which makes simulation performance predictable; 2) it increases the sustained write throughput by a factor of 15 compared to standard approaches; 3) it allows almost perfect scalability of the simulation up to over 9,000 cores, as opposed to state-of-the-art approaches which fail to scale; 4) it enables a 600% compression ratio without any additional overhead, leading to a major reduction of storage requirements.

As data volumes increase at a high speed in more and more application fields of science, engineering, information services, etc., the challenges posed by data-intensive computing gain an increasing importance. The emergence of highly scalable infrastructures, e.g. for cloud computing and for petascale computing and beyond introduces additional issues for which scalable data management becomes an immediate need. This paper brings several contributions. First, it proposes a set of principles for designing highly scalable distributed storage systems that are optimized for heavy data access concurrency. In particular, we highlight the potentially large benefits of using versioning in this context. Second, based on these principles, we propose a set of versioning algorithms, both for data and metadata, that enable a high throughput under concurrency. Finally, we implement and evaluate these algorithms in the BlobSeer prototype, that we integrate as a storage backend in the Hadoop MapReduce framework. We perform extensive microbenchmarks as well as experiments with real MapReduce applications: they demonstrate that applying the principles defended in our approach brings substantial benefits to data intensive applications.