Modeling superscalar processor memory-level parallelism

This paper proposes an analytical model to predict Memory-Level Parallelism (MLP) in a superscalar processor. We profile the workload once and measure a set of distributions to characterize the workload's inherent memory behavior. We subsequently generate a virtual instruction stream, over which we then process an abstract MLP model to predict MLP for a particular micro-architecture with a given ROB size, LLC size, MSHR size and stride-based prefetcher. Experimental evaluation reports an improvement in modeling error from 16.9 percent for previous work to 3.6 percent on average for the proposed model

RPPM : Rapid Performance Prediction of Multithreaded workloads on multicore processors

Analytical performance modeling is a useful complement to detailed cycle-level simulation to quickly explore the design space in an early design stage. Mechanistic analytical modeling is particularly interesting as it provides deep insight and does not require expensive offline profiling as empirical modeling. Previous work in mechanistic analytical modeling, unfortunately, is limited to single-threaded applications running on single-core processors. This work proposes RPPM, a mechanistic analytical performance model for multi-threaded applications on multicore hardware. RPPM collects microarchitecture-independent characteristics of a multi-threaded workload to predict performance on a previously unseen multicore architecture. The profile needs to be collected only once to predict a range of processor architectures. We evaluate RPPM's accuracy against simulation and report a performance prediction error of 11.2% on average (23% max). We demonstrate RPPM's usefulness for conducting design space exploration experiments as well as for analyzing parallel application performance

RPPM : rapid performance prediction of multithreaded applications on multicore hardware

This paper proposes RPPM which, based on a microarchitecture-independent profile of a multithreaded application, predicts its performance on a previously unseen multicore platform. RPPM breaks up multithreaded program execution into epochs based on synchronization primitives, and then predicts per-epoch active execution times for each thread and synchronization overhead to arrive at a prediction for overall application performance. RPPM predicts performance within 12 percent on average (27 percent max error) compared to cycle-level simulation. We present a case study to illustrate that RPPM can be used for making accurate multicore design trade-offs early in the design cycle

Improved methods for haemozoin quantification in tissues yield organ- and parasite-specific information in malariainfected mice

ABSTRACT: BACKGROUND: Despite intensive research, malaria remains a major health concern for non-immune residents and travelers in malaria-endemic regions. Efficient adjunctive therapies against lifethreatening complications such as severe malarial anaemia, encephalopathy, placental malaria or respiratory problems are still lacking. Therefore, new insights into the pathogenesis of severe malaria are imperative. Haemozoin (Hz) or malaria pigment is produced during intraerythrocytic parasite replication, released in the circulation after schizont rupture and accumulates inside multiple organs. Many in vitro and ex vivoimmunomodulating effects are described for Hz but in vivo data are limited. This study aimed to improve methods for Hz quantification in tissues and to investigate the accumulation of Hz in different organs from mice infected with Plasmodium parasites with a varying degree of virulence. METHODS: An improved method for extraction of Hz from tissues was elaborated and coupled to an optimized, quantitative, microtiter plate-based luminescence assay with a high sensitivity. In addition, a technique for measuring Hz by semi-quantitative densitometry, applicable on transmitted light images, was developed. The methods were applied to measure Hz in various organs of C57BL/6J mice infected with Plasmodium berghei ANKA, P. berghei NK65 or Plasmodium chabaudi AS. The used statistical methods were the Mann-Whitney U test and Pearsons correlation analysis. RESULTS: Most Hz was detected in livers and spleens, lower levels in lungs and kidneys, whereas subnanomolar amounts were observed in brains and hearts from infected mice, irrespectively of the parasite strain used. Furthermore, total Hz contents correlated with peripheral parasitaemia and were significantly higher in mice with a lethal P. berghei ANKA or P. berghei NK65-infection than in mice with a self-resolving P. chabaudi AS-infection, despite similar peripheral parasitaemia levels. CONCLUSIONS: The developed techniques were useful to quantify Hz in different organs with a high reproducibility and sensitivity. An organ-specific Hz deposition pattern was found and was independent of the parasite strain used. Highest Hz levels were identified in mice infected with lethal parasite strains suggesting that Hz accumulation in tissues is associated with malaria-related mortality.status: publishe

Micro-architecture independent analytical processor performance and power modeling

Optimizing processors for specific application(s) can substantially improve energy-efficiency. With the end of Dennard scaling, and the corresponding reduction in energyefficiency gains from technology scaling, such approaches may become increasingly important. However, designing applicationspecific processors require fast design space exploration tools to optimize for the targeted application(s). Analytical models can be a good fit for such design space exploration as they provide fast performance estimations and insight into the interaction between an application’s characteristics and the micro-architecture of a processor. Unfortunately, current analytical models require some microarchitecture dependent inputs, such as cache miss rates, branch miss rates and memory-level parallelism. This requires profiling the applications for each cache and branch predictor configuration, which is far more time-consuming than evaluating the actual performance models. In this work we present a micro-architecture independent profiler and associated analytical models that allow us to produce performance and power estimates across a large design space almost instantaneously. We show that using a micro-architecture independent profile leads to a speedup of 25% for our evaluated design space, compared to an analytical model that uses micro-architecture dependent profiles. Over a large design space, the model has a 13% error for performance and a 7% error for power, compared to cycle-level simulation. The model is able to accurately determine the optimal processor configuration for different applications under power or performance constraints, and it can provide insight into performance through cycle stacks

Analytical processor performance and power modeling using micro-architecture independent characteristics

Optimizing processors for (a) specific application(s) can substantially improve energy-efficiency. With the end of Dennard scaling, and the corresponding reduction in energy-efficiency gains from technology scaling, such approaches may become increasingly important. However, designing application-specific processors requires fast design space exploration tools to optimize for the targeted application(s). Analytical models can be a good fit for such design space exploration as they provide fast performance and power estimates and insight into the interaction between an application’s characteristics and the micro-architecture of a processor. Unfortunately, prior analytical models for superscalar out-of-order processors require micro-architecture dependent inputs, such as cache miss rates, branch miss rates and memory-level parallelism. This requires profiling the applications for each cache and branch predictor configuration of interest, which is far more time-consuming than evaluating the analytical performance models. In this work we present a micro-architecture independent profiler and associated analytical models that allow us to produce performance and power estimates across a large superscalar out-of-order processor design space almost instantaneously. We show that using a micro-architecture independent profile leads to a speedup of 300 compared to detailed simulation for our evaluated design space. Over a large design space, the model has a 9.3% average error for performance and a 4.3% average error for power, compared to detailed cycle-level simulation. The model is able to accurately determine the optimal processor configuration for different applications under power or performance constraints, and provides insight into performance through cycle stacks

Noise in the intensive care unit and its influence on sleep quality:A multicenter observational study in Dutch intensive care units

\u3cp\u3eBackground: High noise levels in the intensive care unit (ICU) are a well-known problem. Little is known about the effect of noise on sleep quality in ICU patients. The study aim is to determine the effect of noise on subjective sleep quality. Methods: This was a multicenter observational study in six Dutch ICUs. Noise recording equipment was installed in 2-4 rooms per ICU. Adult patients were eligible for the study 48 h after ICU admission and were followed up to maximum of five nights in the ICU. Exclusion criteria were presence of delirium and/or inability to be assessed for sleep quality. Sleep was evaluated using the Richards Campbell Sleep Questionnaire (range 0-100 mm). Noise recordings were used for analysis of various auditory parameters, including the number and duration of restorative periods. Hierarchical mixed model regression analysis was used to determine associations between noise and sleep. Results: In total, 64 patients (68% male), mean age 63.9 (± 11.7) years and mean Acute Physiology And Chronic Health Evaluation (APACHE) II score 21.1 (± 7.1) were included. Average sleep quality score was 56 ± 24 mm. The mean of the 24-h average sound pressure levels (L\u3csub\u3eAeq, 24h\u3c/sub\u3e) was 54.0 dBA (± 2.4). Mixed-effects regression analyses showed that background noise (β =-0.51, p < 0.05) had a negative impact on sleep quality, whereas number of restorative periods (β = 0.53, p < 0.01) and female sex (β = 1.25, p < 0.01) were weakly but significantly correlated with sleep. Conclusions: Noise levels are negatively associated and restorative periods and female gender are positively associated with subjective sleep quality in ICU patients. Trial registration: Www.ClinicalTrials.gov, NCT01826799. Registered on 9 April 2013.\u3c/p\u3

Noise in the intensive care unit and its influence on sleep quality: a multicenter observational study in Dutch intensive care units

Abstract Background High noise levels in the intensive care unit (ICU) are a well-known problem. Little is known about the effect of noise on sleep quality in ICU patients. The study aim is to determine the effect of noise on subjective sleep quality. Methods This was a multicenter observational study in six Dutch ICUs. Noise recording equipment was installed in 2–4 rooms per ICU. Adult patients were eligible for the study 48 h after ICU admission and were followed up to maximum of five nights in the ICU. Exclusion criteria were presence of delirium and/or inability to be assessed for sleep quality. Sleep was evaluated using the Richards Campbell Sleep Questionnaire (range 0–100 mm). Noise recordings were used for analysis of various auditory parameters, including the number and duration of restorative periods. Hierarchical mixed model regression analysis was used to determine associations between noise and sleep. Results In total, 64 patients (68% male), mean age 63.9 (± 11.7) years and mean Acute Physiology And Chronic Health Evaluation (APACHE) II score 21.1 (± 7.1) were included. Average sleep quality score was 56 ± 24 mm. The mean of the 24-h average sound pressure levels (LAeq, 24h) was 54.0 dBA (± 2.4). Mixed-effects regression analyses showed that background noise (β = − 0.51, p < 0.05) had a negative impact on sleep quality, whereas number of restorative periods (β = 0.53, p < 0.01) and female sex (β = 1.25, p < 0.01) were weakly but significantly correlated with sleep. Conclusions Noise levels are negatively associated and restorative periods and female gender are positively associated with subjective sleep quality in ICU patients. Trial registration www.ClinicalTrials.gov, NCT01826799. Registered on 9 April 2013