A Sampling Microarchitecture Simulator for Java Workloads

Workshop on Tools, Infrastructures and Methodologies for the Evaluation of Research Systems (TIMERS-1) held in conjunction with the International Symposium on Performance Analysis of Systems and Software (ISPASS-2008) : April 20-22, 2008 : Austin, TexasJava has found widespread adoption across a variety of architectures. Understanding Java application behavior and further design and development of Java systems can be facilitated by software based microarchitecture simulators. However, the use of cycle-accurate, user-mode, software microarchitecture simulators in Java characterization studies are scarce and can be attributed to the following reasons: (1) simulating Java applications require the simulator to implement additional features necessary to support the Java runtime which allows dynamic compilation, thread scheduling and garbage collection, (2) the lack of such a simulator validated against actual hardware and its inability to support contemporary Java applications, (3) the complexity of Java applications and the intricate hardware that needs to be modelled result in impractically long simulation time for a single full run of the application, in turn adversely affecting the design and development time for Java-based systems. This paper seeks to address the impediments highlighted above. We enhance the dynamic simplescalar (DSS) simulator to support contemporary Java benchmark workloads. DSS is an out of order superscalar simulator for the PowerPC instruction set architecture and implements features required to support the Java runtime. In order to mitigate simulation time with minimal loss of accuracy, we implement statistical simulation sampling in the DSS simulator. We employ systematic sampling to measure in detail, only a small portion of the entire application being simulated. The application of established statistical sampling techniques allows us to evaluate performance parameters to the desired accuracy and allows us to attribute confidence levels to our estimates of performance. Finally, we validate our enhanced simulator against actual PowerPC hardware using its on-chip performance monitoring unit. Results show that our implementation of statistical sampling in DSS is able to track actual machine performance and achieves an average speedup of over 12x when simulating Java applications. Our validated simulator should help system designers accelerate microarchitecture design space exploration of Java applications

A carbon cycling model shows strong control of seasonality and importance of sponges on the functioning of a northern Red Sea coral reef

Coral reefs in the northern Red Sea experience strong seasonality. This affects reef carbon (C) cycling, but ecosystem-wide quantification of C fluxes in such reefs is limited. This study quantified seasonal reef community C fluxes with incubations. Resulting data were then incorporated into seasonal linear inverse models (LIM). For spring, additional sponge incubation results allowed for unique assessment of the contribution of sponges to C cycling. The coral reef ecosystem was heterotrophic throughout all seasons as gross community primary production (GPP; 136–200, range of seasonal means in mmol C m−2 d−1) was less than community respiration (R; 192–279), and balanced by import of organic carbon (52–100), 88‒92% of which being dissolved organic carbon (DOC). Hard coral GPP (74–110) and R (100–137), as well as pelagic bacteria DOC uptake (58–101) and R (42–86), were the largest C fluxes across seasons. The ecosystem was least heterotrophic in spring (highest irradiance) (GPP:R 0.81), but most heterotrophic in summer and fall with higher water temperatures (0.68 and 0.60, respectively). Adding the sponge community to the model increased community R (247 ± 8 without to 353 ± 13 with sponges (mean ± SD)). Sponges balanced this demand primarily with DOC uptake (105 ± 6, 97% by cryptic sponges). This rate is comparable to the uptake of DOC by pelagic bacteria (104 ± 5) placing the cryptic sponges among the dominant C cycling groups in the reef.</p

Allochthonous bioaugmentation in ex situ treatment of crude oil-polluted sediments in the presence of an effective degrading indigenous microbiome

Oil-polluted sediment bioremediation depends on both physicochemical and biological parameters, but the effect of the latter cannot be evaluated without the optimization of the former. We aimed in optimizing the physicochemical parameters related to biodegradation by applying an ex-situ landfarming set-up combined with biostimulation to oil-polluted sediment, in order to determine the added effect of bioaugmentation by four allochthonous oil-degrading bacterial consortia in relation to the degradation efficiency of the indigenous community. We monitored hydrocarbon degradation, sediment ecotoxicity and hydrolytic activity, bacterial population sizes and bacterial community dynamics, characterizing the dominant taxa through time and at each treatment. We observed no significant differences in total degradation, but increased ecotoxicity between the different treatments receiving both biostimulation and bioaugmentation and the biostimulated-only control. Moreover, the added allochthonous bacteria quickly perished and were rarely detected, their addition inducing minimal shifts in community structure although it altered the distribution of the residual hydrocarbons in two treatments. Therefore, we concluded that biodegradation was mostly performed by the autochthonous populations while bioaugmentation, in contrast to biostimulation, did not enhance the remediation process. Our results indicate that when environmental conditions are optimized, the indigenous microbiome at a polluted site will likely outperform any allochthonous consortium