Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

Hijacker: Efficient static software instrumentation with applications in high performance computing: Poster paper

Static Binary Instrumentation is a technique that allows compile-time program manipulation. In particular, by relying on ad-hoc tools, the end user is able to alter the program's execution flow without affecting its overall semantic. This technique has been effectively used, e.g., to support code profiling, performance analysis, error detection, attack detection, or behavior monitoring. Nevertheless, efficiently relying on static instrumentation for producing executables which can be deployed without affecting the overall performance of the application still presents technical and methodological issues. In this paper, we present Hijacker, an open-source customizable static binary instrumentation tool which is able to alter a program's execution flow according to some user-specified rules, limiting the execution overhead due to the code snippets inserted in the original program, thus enabling for the exploitation in high performance computing. The tool is highly modular and works on an internal representation of the program which allows to perform complex instrumentation tasks efficiently, and can be additionally extended to support different instruction sets and executable formats without any need to modify the instrumentation engine. We additionally present an experimental assessment of the overhead induced by the injected code in real HPC applications. © 2013 IEEE

A Template for Implementing Fast Lock-free Trees Using HTM

Algorithms that use hardware transactional memory (HTM) must provide a software-only fallback path to guarantee progress. The design of the fallback path can have a profound impact on performance. If the fallback path is allowed to run concurrently with hardware transactions, then hardware transactions must be instrumented, adding significant overhead. Otherwise, hardware transactions must wait for any processes on the fallback path, causing concurrency bottlenecks, or move to the fallback path. We introduce an approach that combines the best of both worlds. The key idea is to use three execution paths: an HTM fast path, an HTM middle path, and a software fallback path, such that the middle path can run concurrently with each of the other two. The fast path and fallback path do not run concurrently, so the fast path incurs no instrumentation overhead. Furthermore, fast path transactions can move to the middle path instead of waiting or moving to the software path. We demonstrate our approach by producing an accelerated version of the tree update template of Brown et al., which can be used to implement fast lock-free data structures based on down-trees. We used the accelerated template to implement two lock-free trees: a binary search tree (BST), and an (a,b)-tree (a generalization of a B-tree). Experiments show that, with 72 concurrent processes, our accelerated (a,b)-tree performs between 4.0x and 4.2x as many operations per second as an implementation obtained using the original tree update template