Synapse: Synthetic Application Profiler and Emulator

We introduce Synapse motivated by the needs to estimate and emulate workload execution characteristics on high-performance and distributed heterogeneous resources. Synapse has a platform independent application profiler, and the ability to emulate profiled workloads on a variety of heterogeneous resources. Synapse is used as a proxy application (or "representative application") for real workloads, with the added advantage that it can be tuned at arbitrary levels of granularity in ways that are simply not possible using real applications. Experiments show that automated profiling using Synapse represents application characteristics with high fidelity. Emulation using Synapse can reproduce the application behavior in the original runtime environment, as well as reproducing properties when used in a different run-time environments

Performance Analysis and Optimization of Sparse Matrix-Vector Multiplication on Modern Multi- and Many-Core Processors

This paper presents a low-overhead optimizer for the ubiquitous sparse matrix-vector multiplication (SpMV) kernel. Architectural diversity among different processors together with structural diversity among different sparse matrices lead to bottleneck diversity. This justifies an SpMV optimizer that is both matrix- and architecture-adaptive through runtime specialization. To this direction, we present an approach that first identifies the performance bottlenecks of SpMV for a given sparse matrix on the target platform either through profiling or by matrix property inspection, and then selects suitable optimizations to tackle those bottlenecks. Our optimization pool is based on the widely used Compressed Sparse Row (CSR) sparse matrix storage format and has low preprocessing overheads, making our overall approach practical even in cases where fast decision making and optimization setup is required. We evaluate our optimizer on three x86-based computing platforms and demonstrate that it is able to distinguish and appropriately optimize SpMV for the majority of matrices in a representative test suite, leading to significant speedups over the CSR and Inspector-Executor CSR SpMV kernels available in the latest release of the Intel MKL library.Comment: 10 pages, 7 figures, ICPP 201

Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma.

Lipidomics - the global assessment of lipids - can be performed using a variety of mass spectrometry (MS)-based approaches. However, choosing the optimal approach in terms of lipid coverage, robustness and throughput can be a challenging task. Here, we compare a novel targeted quantitative lipidomics platform known as the Lipidyzer to a conventional untargeted liquid chromatography (LC)-MS approach. We find that both platforms are efficient in profiling more than 300 lipids across 11 lipid classes in mouse plasma with precision and accuracy below 20% for most lipids. While the untargeted and targeted platforms detect similar numbers of lipids, the former identifies a broader range of lipid classes and can unambiguously identify all three fatty acids in triacylglycerols (TAG). Quantitative measurements from both approaches exhibit a median correlation coefficient (r) of 0.99 using a dilution series of deuterated internal standards and 0.71 using endogenous plasma lipids in the context of aging. Application of both platforms to plasma from aging mouse reveals similar changes in total lipid levels across all major lipid classes and in specific lipid species. Interestingly, TAG is the lipid class that exhibits the most changes with age, suggesting that TAG metabolism is particularly sensitive to the aging process in mice. Collectively, our data show that the Lipidyzer platform provides comprehensive profiling of the most prevalent lipids in plasma in a simple and automated manner

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it

Iso-energy-efficiency: An approach to power-constrained parallel computation

Future large scale high performance supercomputer systems require high energy efficiency to achieve exaflops computational power and beyond. Despite the need to understand energy efficiency in high-performance systems, there are few techniques to evaluate energy efficiency at scale. In this paper, we propose a system-level iso-energy-efficiency model to analyze, evaluate and predict energy-performance of data intensive parallel applications with various execution patterns running on large scale power-aware clusters. Our analytical model can help users explore the effects of machine and application dependent characteristics on system energy efficiency and isolate efficient ways to scale system parameters (e.g. processor count, CPU power/frequency, workload size and network bandwidth) to balance energy use and performance. We derive our iso-energy-efficiency model and apply it to the NAS Parallel Benchmarks on two power-aware clusters. Our results indicate that the model accurately predicts total system energy consumption within 5% error on average for parallel applications with various execution and communication patterns. We demonstrate effective use of the model for various application contexts and in scalability decision-making

An Extensible Timing Infrastructure for Adaptive Large-scale Applications

Real-time access to accurate and reliable timing information is necessary to profile scientific applications, and crucial as simulations become increasingly complex, adaptive, and large-scale. The Cactus Framework provides flexible and extensible capabilities for timing information through a well designed infrastructure and timing API. Applications built with Cactus automatically gain access to built-in timers, such as gettimeofday and getrusage, system-specific hardware clocks, and high-level interfaces such as PAPI. We describe the Cactus timer interface, its motivation, and its implementation. We then demonstrate how this timing information can be used by an example scientific application to profile itself, and to dynamically adapt itself to a changing environment at run time

Metabolomic profiling of macrophages determines the discrete metabolomic signature and metabolomic interactome triggered by polarising immune stimuli

Priming and activating immune stimuli have profound effects on macrophages, however, studies generally evaluate stimuli in isolation rather than in combination. In this study we have investigated the effects of pro-inflammatory and anti-inflammatory stimuli either alone or in combination on macrophage metabolism. These stimuli include host factors such as IFNγ and ovalbumin-immunoglobulin immune complexes, or pathogen factors such as LPS. Untargeted LC-MS based metabolomics provided an in-depth profile of the macrophage metabolome, and revealed specific changes in metabolite abundance upon either individual stimuli or combined stimuli. Here, by factoring in an interaction term in the linear model, we define the metabolome interactome. This approach allowed us to determine whether stimuli interact in a synergistic or antagonistic manner. In conclusion this study demonstrates a robust approach to interrogate immune-metabolism, especially systems that model host-pathogen interactions

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included