Large caches are necessary in current high-performance computer systems to provide the required high memory bandwidth. Because a small decrease in cache performance can result in significant system performance degradation, accurately characterizing the performance of large caches is important. Although measurements on actual systems have shown that operating systems and multiprogramming can affect cache performance, previous studies have not focused on these effects. We have developed a program tracing technique called ATUM (Address Tracing Using Microcode) that captures realistic traces of multitasking workloads including the operating system. Examining cache behavior using these traces from a VAX processor shows that both the operating system and multiprogramming activity significantly degrade cache performance, with an even greater proportional impact on large caches. From a careful analysis of the causes of this degradation, we explore various techniques to reduce this loss. While seemingly little can be done to mitigate the effect of system references, multitasking cache miss activity can be substantially reduced with small hardware additions.
Trace-driven simulation and hardware measurement are the techniques most often used to obtain accurate performance figures for caches. The former requires a large amount of simulation time to evaluate each cache configuration while the latter is restricted to measurements of existing caches. An analytical cache model that uses parameters extracted from address traces of programs can efficiently provide estimates of cache performance and show the effects of varying cache parameters. By representing the factors that affect cache performance, we develop an analytical model that gives miss rates for a given trace as a function of cache size, degree of associativity, block size, subblock size, multiprogramming level, task switch interval, and observation interval. The predicted values closely approximate the results of trace-driven simulations, while requiring only a small fraction of the computation cost.
Eur J Clin Invest 2012; 42 (3): 233–244 Abstract Background Systemic corticosteroids have been proposed for numerous indications and there are many claims that corticosteroids can reduce mortality in diverse conditions. Methods We performed an umbrella, agenda‐wide review of the evidence on systemic corticosteroids and mortality, focusing primarily on large trials (defined as those with > 100 deaths) and meta‐analyses. Searches were performed in P ub M ed and Cochrane Central Register of Controlled Trials (last update February 2011). We also examined whether spurious subset analyses may be responsible for claims of survival benefits in indications where only small trials had been available. Results Among 257 identified randomized trials with mortality data in their abstract, we found 14 large trials pertaining to 10 different indications. Although 10 of these 14 trials have reported statistically significant survival differences in subset analyses, none shows a nominally statistically significant ( P < 0·05) decrease in death risk for any of the tested conditions when all deaths on all randomized patients are analysed. Meta‐analyses for these conditions show statistically significant reductions in mortality only with antenatal corticosteroids for preterm labour (relative risk 0·77, 95% CI, 0·67–0·89) and in tuberculous meningitis (relative risk 0·78, 95% CI, 0·67–0·91). For conditions without any large trials, statistically significant reductions in mortality in meta‐analyses were noted for Pneumocystis pneumonia (relative risk 0·54, 95% CI, 0·38–0·79) and alcoholic hepatitis (relative risk 0·63, 95% CI, 0·50–0·80). Many small trials that claim significant benefits, even those for classic indications such as typhoid fever and tetanus, have shown these benefits only in subset analyses. Conclusions Corticosteroids have been documented to decrease mortality in some indications, in particular, antenatal use for preterm labour, tuberculous meningitis, Pneumocystis pneumonia, and alcoholic hepatitis. Many postulated benefits of corticosteroids on mortality may reflect ‘vibration of treatment effects’ leading to false‐positive claims from spurious subset analyses and even for standard indications, such biases may have inflated the treatment effect estimates. More large trials are needed for serious, common conditions where use of corticosteroids is proposed.
Kaiser Family Foundation; Adam Wexler, MPP; Alison Valentine; Jennifer Kates, PhD; Anne Jankiewicz; David Rousseau, MPH
Abstract Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of psychology, cognitive neuroscience and biomedical science in general. We review these shortcomings and suggest that, after about 60 years of negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. Different inferential methods (NHST, likelihood estimation, Bayesian methods, false-discovery rate control) may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Studies should optimally be pre-registered and raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out. Instead, we should encourage either more in-depth statistical training of more researchers and/or more widespread involvement of professional statisticians in all research.
Predicting the future is notoriously hard. Sometimes I feel that the only real guarantee is that the future will happen, and that someone will point out how it’s not like what was predicted. Nevertheless, we seem intent on trying to figure out what will happen, and worse yet, recording these views so they can be later used against us. So here I go... Scaling has been driving the whole electronics industry, allowing it to produce chips with more transistors at a lower cost. But this trend is a double-edged sword: We not only need to figure out more complex devices, which people want, but we also must determine which complex devices lots of people want, as we have to sell many, many chips to amortize the significant design cost.
Abstract Several teams have been publishing global estimates of excess deaths during the COVID‐19 pandemic. Here, we examine potential flaws and underappreciated sources of uncertainty in global excess death calculations. Adjusting for changing population age structure is essential. Otherwise, excess deaths are markedly overestimated in countries with increasingly aging populations. Adjusting for changes in other high‐risk indicators, such as residence in long‐term facilities, may also make a difference. Death registration is highly incomplete in most countries; completeness corrections should allow for substantial uncertainty and consider that completeness may have changed during pandemic years. Excess death estimates have high sensitivity to modelling choice. Therefore different options should be considered and the full range of results should be shown for different choices of pre‐pandemic reference periods and imposed models. Any post‐modelling corrections in specific countries should be guided by pre‐specified rules. Modelling of all‐cause mortality (ACM) in countries that have ACM data and extrapolating these models to other countries is precarious; models may lack transportability. Existing global excess death estimates underestimate the overall uncertainty that is multiplicative across diverse sources of uncertainty. Informative excess death estimates require risk stratification, including age groups and ethnic/racial strata. Data to‐date suggest a death deficit among children during the pandemic and marked socioeconomic differences in deaths, widening inequalities. Finally, causal explanations require great caution in disentangling SARS‐CoV‐2 deaths, indirect pandemic effects and effects from measures taken. We conclude that excess deaths have many uncertainties, but globally deaths from SARS‐CoV‐2 may be the minority of calculated excess deaths.
Although limited in quantity, existing randomised trial evidence on exercise interventions suggests that exercise and many drug interventions are often potentially similar in terms of their mortality benefits in the secondary prevention of coronary heart disease, rehabilitation after stroke, treatment of heart failure, and prevention of diabetes.
