Tag : Performance

4 Ways To Predict Market Performance #small #business #advice


#stock market results

#

4 Ways To Predict Market Performance

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There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won’t buy a stock or index that has risen too sharply, because they assume that it’s due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate.

Does academic evidence support these types of predictions, based on recent pricing? In this article, we’ll look at four different views of the market and learn more about the associated academic research that supports each view. The conclusions will help you better understand how the market functions, and perhaps eliminate some of your own biases.

Momentum
“Don’t fight the tape.” This widely quoted piece of stock market wisdom warns investors not to get in the way of market trends. The assumption is that the best bet about market movements is that they will continue in the same direction. This concept has is roots in behavioral finance. With so many stocks to choose from, why would investors keep their money in a stock that’s falling, as opposed to one that’s climbing? It’s classic fear and greed. (For more insight, see the Behavioral Finance tutorial.)

Studies have found that mutual fund inflows are positively correlated with market returns. Momentum plays a part in the decision to invest and when more people invest, the market goes up, encouraging even more people to buy. It’s a positive feedback loop.

A 1993 study by Narasimhan Jagadeesh and Sheridan Titman, “Returns to Buying Winners and Selling Losers,” suggests that individual stocks have momentum. They found that stocks that have performed well during the past few months, are more likely to continue their outperformance next month. The inverse also applies; stocks that have performed poorly, are more likely to continue their poor performance.

However, this study only looked ahead a single month. Over longer periods, the momentum effect appears to reverse. According to a 1985 study by Werner DeBondt and Richard Thaler, “Does the Stock Market Overreact?” stocks that have performed well in the past three to five years are more likely to underperform the market in the next three to five years and vice versa. This suggests that something else is going on: mean reversion .

Mean Reversion
Experienced investors who have seen many market ups and downs, often take the view that the market will even out, over time. Historically high market prices often discourage these investors from investing, while historically low prices may represent an opportunity.

The tendency of a variable, such as a stock price, to converge on an average value over time is called mean reversion. The phenomenon has been found in several economic indicators. including exchange rates. gross domestic product (GDP) growth, interest rates and unemployment. Mean reversion may also be responsible for business cycles. (For more insight, check out Economic Indicators To Know and Economic Indicators For The Do-It-Yoursel Investor .)

The research is still inconclusive about whether stock prices revert to the mean. Some studies show mean reversion in some data sets over some periods, but many others do not. For example, in 2000, Ronald Balvers, Yangru Wu and Erik Gilliland found some evidence of mean reversion over long investment horizons. in the relative stock index prices of 18 countries, which they described in the “Journal of Finance.”

However, even they weren’t completely convinced, as they wrote in their study, “A serious obstacle in detecting mean reversion is the absence of reliable long-term series, especially because mean-reversion, if it exists, is thought to be slow and can only be picked up over long horizons.”

Given that academia has access to at least 80 years of stock market research. this suggests that if the market does have a tendency to mean revert, it is a phenomenon that happens slowly and almost imperceptibly, over many years or even decades.

Martingales
Another possibility is that past returns just don’t matter. In 1965, Paul Samuelson studied market returns and found that past pricing trends had no effect on future prices and reasoned that in an efficient market. there should be no such effect. His conclusion was that market prices are martingales. (To read more, see Working Through The Efficient Market Hypothesis .)

A martingale is a mathematical series in which the best prediction for the next number is the current number. The concept is used in probability theory, to estimate the results of random motion. For example, suppose that you have $50 and bet it all on a coin toss. How much money will you have after the toss? You may have $100 or you may have $0 after the toss, but statistically the best prediction is $50; your original starting position. The prediction of your fortunes after the toss is a martingale. (To learn how this applies to trading, see Forex Trading The Martingale Way .)

In stock option pricing, stock market returns could be assumed to be martingales. According to this theory, the valuation of the option does not depend on the past pricing trend, or on any estimate of future price trends. The current price and the estimated volatility are the only stock-specific inputs.

A martingale in which the next number is more likely to be higher, is known as a sub-martingale. In popular literature, this motion is known as a random walk with upward drift. This description is consistent with the more than 80 years of stock market pricing history. Despite many short-term reversals. the overall trend has been consistently higher. (To learn more about random walk, read Financial Concepts: Random Walk .)

If stock returns are essentially random, the best prediction for tomorrow’s market price is simply today’s price, plus a very small increase. Rather than focusing on past trends and looking for possible momentum or mean reversion, investors should instead concentrate on managing the risk inherent in their volatile investments.

The Search for Value
Value investors purchase stock cheaply and expect to be rewarded later. Their hope is that an inefficient market has underpriced the stock, but that the price will adjust over time. The question is does this happen and why would an inefficient market make this adjustment?

Research suggests that this mispricing and readjustment consistently happens, although it presents very little evidence for why it happens.

In 1964, Gene Fama and Ken French studied decades of stock market history and developed the three-factor model to explain stock market prices. The most significant factor in explaining future price returns was valuation, as measured by the price-to-book ratio. Stocks with low price-to-book ratios delivered significantly better returns than other stocks. (To read more about this ratio, see Value By The Book .)

Valuation ratios tend to move in the same direction and in 1977, Sanjoy Basu found similar results for stocks with low price-earnings (P/E) ratios. Since then, the same effect has been found in many other studies across dozens of markets. (For more on this, check out Understanding The P/E Ratio .)

However, studies have not explained why the market is consistently mispricing these “value” stocks and then adjusting later. The only conclusion that could be drawn is that these stocks have extra risk, for which investors demand additional compensation. (To learn more about this phenomenon, read The Equity-Risk Premium: More Risk For Higher Returns and Calculating The Equity Risk Premium .)

Price is the driver of the valuation ratios, therefore, the findings do support the idea of a mean-reverting stock market. As prices climb, the valuation ratios get higher and, as a result, future predicted returns are lower. However, the market P/E ratio has fluctuated widely over time and has never been a consistent buy or sell signal .

The Bottom Line
Even after decades of study by the brightest minds in finance, there are no solid answers. The only conclusion that can be drawn is that there may be some momentum effects, in the short term. and a weak mean reversion effect, in the long term.

The current price is a key component of valuation ratios such as P/B and P/E, that have been shown to have some predictive power on the future returns of a stock. However, these ratios should not be viewed as specific buy and sell signals, just factors that have been shown to play a role in increasing or reducing the expected long-term return.



4 Ways To Predict Market Performance #best #business #to #start


#stock market results

#

4 Ways To Predict Market Performance

Loading the player.

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won’t buy a stock or index that has risen too sharply, because they assume that it’s due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate.

Does academic evidence support these types of predictions, based on recent pricing? In this article, we’ll look at four different views of the market and learn more about the associated academic research that supports each view. The conclusions will help you better understand how the market functions, and perhaps eliminate some of your own biases.

Momentum
“Don’t fight the tape.” This widely quoted piece of stock market wisdom warns investors not to get in the way of market trends. The assumption is that the best bet about market movements is that they will continue in the same direction. This concept has is roots in behavioral finance. With so many stocks to choose from, why would investors keep their money in a stock that’s falling, as opposed to one that’s climbing? It’s classic fear and greed. (For more insight, see the Behavioral Finance tutorial.)

Studies have found that mutual fund inflows are positively correlated with market returns. Momentum plays a part in the decision to invest and when more people invest, the market goes up, encouraging even more people to buy. It’s a positive feedback loop.

A 1993 study by Narasimhan Jagadeesh and Sheridan Titman, “Returns to Buying Winners and Selling Losers,” suggests that individual stocks have momentum. They found that stocks that have performed well during the past few months, are more likely to continue their outperformance next month. The inverse also applies; stocks that have performed poorly, are more likely to continue their poor performance.

However, this study only looked ahead a single month. Over longer periods, the momentum effect appears to reverse. According to a 1985 study by Werner DeBondt and Richard Thaler, “Does the Stock Market Overreact?” stocks that have performed well in the past three to five years are more likely to underperform the market in the next three to five years and vice versa. This suggests that something else is going on: mean reversion .

Mean Reversion
Experienced investors who have seen many market ups and downs, often take the view that the market will even out, over time. Historically high market prices often discourage these investors from investing, while historically low prices may represent an opportunity.

The tendency of a variable, such as a stock price, to converge on an average value over time is called mean reversion. The phenomenon has been found in several economic indicators. including exchange rates. gross domestic product (GDP) growth, interest rates and unemployment. Mean reversion may also be responsible for business cycles. (For more insight, check out Economic Indicators To Know and Economic Indicators For The Do-It-Yoursel Investor .)

The research is still inconclusive about whether stock prices revert to the mean. Some studies show mean reversion in some data sets over some periods, but many others do not. For example, in 2000, Ronald Balvers, Yangru Wu and Erik Gilliland found some evidence of mean reversion over long investment horizons. in the relative stock index prices of 18 countries, which they described in the “Journal of Finance.”

However, even they weren’t completely convinced, as they wrote in their study, “A serious obstacle in detecting mean reversion is the absence of reliable long-term series, especially because mean-reversion, if it exists, is thought to be slow and can only be picked up over long horizons.”

Given that academia has access to at least 80 years of stock market research. this suggests that if the market does have a tendency to mean revert, it is a phenomenon that happens slowly and almost imperceptibly, over many years or even decades.

Martingales
Another possibility is that past returns just don’t matter. In 1965, Paul Samuelson studied market returns and found that past pricing trends had no effect on future prices and reasoned that in an efficient market. there should be no such effect. His conclusion was that market prices are martingales. (To read more, see Working Through The Efficient Market Hypothesis .)

A martingale is a mathematical series in which the best prediction for the next number is the current number. The concept is used in probability theory, to estimate the results of random motion. For example, suppose that you have $50 and bet it all on a coin toss. How much money will you have after the toss? You may have $100 or you may have $0 after the toss, but statistically the best prediction is $50; your original starting position. The prediction of your fortunes after the toss is a martingale. (To learn how this applies to trading, see Forex Trading The Martingale Way .)

In stock option pricing, stock market returns could be assumed to be martingales. According to this theory, the valuation of the option does not depend on the past pricing trend, or on any estimate of future price trends. The current price and the estimated volatility are the only stock-specific inputs.

A martingale in which the next number is more likely to be higher, is known as a sub-martingale. In popular literature, this motion is known as a random walk with upward drift. This description is consistent with the more than 80 years of stock market pricing history. Despite many short-term reversals. the overall trend has been consistently higher. (To learn more about random walk, read Financial Concepts: Random Walk .)

If stock returns are essentially random, the best prediction for tomorrow’s market price is simply today’s price, plus a very small increase. Rather than focusing on past trends and looking for possible momentum or mean reversion, investors should instead concentrate on managing the risk inherent in their volatile investments.

The Search for Value
Value investors purchase stock cheaply and expect to be rewarded later. Their hope is that an inefficient market has underpriced the stock, but that the price will adjust over time. The question is does this happen and why would an inefficient market make this adjustment?

Research suggests that this mispricing and readjustment consistently happens, although it presents very little evidence for why it happens.

In 1964, Gene Fama and Ken French studied decades of stock market history and developed the three-factor model to explain stock market prices. The most significant factor in explaining future price returns was valuation, as measured by the price-to-book ratio. Stocks with low price-to-book ratios delivered significantly better returns than other stocks. (To read more about this ratio, see Value By The Book .)

Valuation ratios tend to move in the same direction and in 1977, Sanjoy Basu found similar results for stocks with low price-earnings (P/E) ratios. Since then, the same effect has been found in many other studies across dozens of markets. (For more on this, check out Understanding The P/E Ratio .)

However, studies have not explained why the market is consistently mispricing these “value” stocks and then adjusting later. The only conclusion that could be drawn is that these stocks have extra risk, for which investors demand additional compensation. (To learn more about this phenomenon, read The Equity-Risk Premium: More Risk For Higher Returns and Calculating The Equity Risk Premium .)

Price is the driver of the valuation ratios, therefore, the findings do support the idea of a mean-reverting stock market. As prices climb, the valuation ratios get higher and, as a result, future predicted returns are lower. However, the market P/E ratio has fluctuated widely over time and has never been a consistent buy or sell signal .

The Bottom Line
Even after decades of study by the brightest minds in finance, there are no solid answers. The only conclusion that can be drawn is that there may be some momentum effects, in the short term. and a weak mean reversion effect, in the long term.

The current price is a key component of valuation ratios such as P/B and P/E, that have been shown to have some predictive power on the future returns of a stock. However, these ratios should not be viewed as specific buy and sell signals, just factors that have been shown to play a role in increasing or reducing the expected long-term return.



4 Ways To Predict Market Performance


#stock market results

#

4 Ways To Predict Market Performance

Loading the player.

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won’t buy a stock or index that has risen too sharply, because they assume that it’s due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate.

Does academic evidence support these types of predictions, based on recent pricing? In this article, we’ll look at four different views of the market and learn more about the associated academic research that supports each view. The conclusions will help you better understand how the market functions, and perhaps eliminate some of your own biases.

Momentum
“Don’t fight the tape.” This widely quoted piece of stock market wisdom warns investors not to get in the way of market trends. The assumption is that the best bet about market movements is that they will continue in the same direction. This concept has is roots in behavioral finance. With so many stocks to choose from, why would investors keep their money in a stock that’s falling, as opposed to one that’s climbing? It’s classic fear and greed. (For more insight, see the Behavioral Finance tutorial.)

Studies have found that mutual fund inflows are positively correlated with market returns. Momentum plays a part in the decision to invest and when more people invest, the market goes up, encouraging even more people to buy. It’s a positive feedback loop.

A 1993 study by Narasimhan Jagadeesh and Sheridan Titman, “Returns to Buying Winners and Selling Losers,” suggests that individual stocks have momentum. They found that stocks that have performed well during the past few months, are more likely to continue their outperformance next month. The inverse also applies; stocks that have performed poorly, are more likely to continue their poor performance.

However, this study only looked ahead a single month. Over longer periods, the momentum effect appears to reverse. According to a 1985 study by Werner DeBondt and Richard Thaler, “Does the Stock Market Overreact?” stocks that have performed well in the past three to five years are more likely to underperform the market in the next three to five years and vice versa. This suggests that something else is going on: mean reversion .

Mean Reversion
Experienced investors who have seen many market ups and downs, often take the view that the market will even out, over time. Historically high market prices often discourage these investors from investing, while historically low prices may represent an opportunity.

The tendency of a variable, such as a stock price, to converge on an average value over time is called mean reversion. The phenomenon has been found in several economic indicators. including exchange rates. gross domestic product (GDP) growth, interest rates and unemployment. Mean reversion may also be responsible for business cycles. (For more insight, check out Economic Indicators To Know and Economic Indicators For The Do-It-Yoursel Investor .)

The research is still inconclusive about whether stock prices revert to the mean. Some studies show mean reversion in some data sets over some periods, but many others do not. For example, in 2000, Ronald Balvers, Yangru Wu and Erik Gilliland found some evidence of mean reversion over long investment horizons. in the relative stock index prices of 18 countries, which they described in the “Journal of Finance.”

However, even they weren’t completely convinced, as they wrote in their study, “A serious obstacle in detecting mean reversion is the absence of reliable long-term series, especially because mean-reversion, if it exists, is thought to be slow and can only be picked up over long horizons.”

Given that academia has access to at least 80 years of stock market research. this suggests that if the market does have a tendency to mean revert, it is a phenomenon that happens slowly and almost imperceptibly, over many years or even decades.

Martingales
Another possibility is that past returns just don’t matter. In 1965, Paul Samuelson studied market returns and found that past pricing trends had no effect on future prices and reasoned that in an efficient market. there should be no such effect. His conclusion was that market prices are martingales. (To read more, see Working Through The Efficient Market Hypothesis .)

A martingale is a mathematical series in which the best prediction for the next number is the current number. The concept is used in probability theory, to estimate the results of random motion. For example, suppose that you have $50 and bet it all on a coin toss. How much money will you have after the toss? You may have $100 or you may have $0 after the toss, but statistically the best prediction is $50; your original starting position. The prediction of your fortunes after the toss is a martingale. (To learn how this applies to trading, see Forex Trading The Martingale Way .)

In stock option pricing, stock market returns could be assumed to be martingales. According to this theory, the valuation of the option does not depend on the past pricing trend, or on any estimate of future price trends. The current price and the estimated volatility are the only stock-specific inputs.

A martingale in which the next number is more likely to be higher, is known as a sub-martingale. In popular literature, this motion is known as a random walk with upward drift. This description is consistent with the more than 80 years of stock market pricing history. Despite many short-term reversals. the overall trend has been consistently higher. (To learn more about random walk, read Financial Concepts: Random Walk .)

If stock returns are essentially random, the best prediction for tomorrow’s market price is simply today’s price, plus a very small increase. Rather than focusing on past trends and looking for possible momentum or mean reversion, investors should instead concentrate on managing the risk inherent in their volatile investments.

The Search for Value
Value investors purchase stock cheaply and expect to be rewarded later. Their hope is that an inefficient market has underpriced the stock, but that the price will adjust over time. The question is does this happen and why would an inefficient market make this adjustment?

Research suggests that this mispricing and readjustment consistently happens, although it presents very little evidence for why it happens.

In 1964, Gene Fama and Ken French studied decades of stock market history and developed the three-factor model to explain stock market prices. The most significant factor in explaining future price returns was valuation, as measured by the price-to-book ratio. Stocks with low price-to-book ratios delivered significantly better returns than other stocks. (To read more about this ratio, see Value By The Book .)

Valuation ratios tend to move in the same direction and in 1977, Sanjoy Basu found similar results for stocks with low price-earnings (P/E) ratios. Since then, the same effect has been found in many other studies across dozens of markets. (For more on this, check out Understanding The P/E Ratio .)

However, studies have not explained why the market is consistently mispricing these “value” stocks and then adjusting later. The only conclusion that could be drawn is that these stocks have extra risk, for which investors demand additional compensation. (To learn more about this phenomenon, read The Equity-Risk Premium: More Risk For Higher Returns and Calculating The Equity Risk Premium .)

Price is the driver of the valuation ratios, therefore, the findings do support the idea of a mean-reverting stock market. As prices climb, the valuation ratios get higher and, as a result, future predicted returns are lower. However, the market P/E ratio has fluctuated widely over time and has never been a consistent buy or sell signal .

The Bottom Line
Even after decades of study by the brightest minds in finance, there are no solid answers. The only conclusion that can be drawn is that there may be some momentum effects, in the short term. and a weak mean reversion effect, in the long term.

The current price is a key component of valuation ratios such as P/B and P/E, that have been shown to have some predictive power on the future returns of a stock. However, these ratios should not be viewed as specific buy and sell signals, just factors that have been shown to play a role in increasing or reducing the expected long-term return.



16 Useful Performance Tools for Java Environments, All Things JVM And Java

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All Things JVM And Java Memory Tuning

16 Useful Performance Tools for Java Environments

This is a listing of 16 performance monitoring utilities related to Java and the JVM. Some here are free open source others are commercial and many are very fully featured. Only one comes from Oracle. I shall be doing another post to list the Oracle supplied tools later.

1 . FusionReactor FusionReactor was originally released as a ColdFusion application server monitor. ColdFusion originally ran on Jrun and now runs on Tomcat-Jetty. FusionReactor is very fully featured and is a commercial product which can monitor Java-JVM memory usage, servlet performance and JDBC database connections. It has a very rich GUI and extensive alerting capabilities.

2. Newrelic – Real-time Java application monitoring, which is free to get started with. It features code deployment reports, transaction tracing across different tiers and the ability to create alerts.

3. Datadog A JMX graphing tool with a free trial but no detail of pricing beyond that.

3. Java Melody Java Melody is a free and lightweight monitoring tool which does not do any profiling so is safe to use in production environments. It comes with a series of plug-ins including for Grails, Jenkins and Jira.

4. Jamon Jamon is a powerful free monitoring tool with a great deal of capabilities including the monitoring of SQL queries, servlets and individual method performance. There is also the capability to add listeners which allows us to action events in the application being monitored.

6. Moskito Moskito is a free open-source Java monitoring tool which is easy to get up and running by dropping the .jar file into the WEB-INF/lib folder or by including a small code section into the web.xml file. There is an included data storage option to keep historical data for later analysis.

7. VisualVM VisualVM has been around for some time and has active version updates still. As of the publication of this blog piece the latest version was released in July 2014. There are a series of plug-ins that work with Netbeans. I f you choose JVM Comparison in the list of samples above, you ll be able to see the system properties of all the applications running on the local JVM.

8. Memory Analyzer (MAT) This is one of a few Oracle supplied utilities and can be used in Eclipse to analyze heap dumps to see the retained size of all objects and also, from that, try to ascertain what is preventing their collection during a Garbage Collection (GC).

9. GCViewer GCViewer is another free tool which has been around for a while and is an excellent utility for analyzing Garbage Collection (GC) logs providing a dearth of empirical details and graphs which can help greatly in the analysis of GC s.

10. Netbeans Profiler – NetBeans profiler is a module to provide a full featured profiling functionality for the NetBeans IDE. The profiling functions include CPU, memory and threads profiling as well as basic JVM monitoring, allowing developers to be more productive in solving memory or performance-related issues.

11. JProfiler – JProfiler is a commercial utility with extensive reporting capabilities for Java applications and most items needed to run them, including JVM memory behavior, databases via JDBC, JPA and NoSQL, system CPU profiler and thread profiler.

12. The Patty Project – The Patty project is aimed at providing a free profiling tool for the Java 1.5.0 and higher Virtual Machines only (depending on backwards compatibility in JVMTI interface). The difference with other profilers is this project maintains a very high emphasis on targeted profiling and allows users to switch profiling features on and off at runtime.

13. JiP JiP stands for Jave interactive profiler – JIP is a free utility that gathers performance data. You cannot use most profilers to do timings of your application. hprof, for example, will show you the relative amount of time that is spent in different parts of your code, but hprof has so much overhead that you cannot use it to get real world timing measurements. JIP, on the other hand, actually tracks the amount of time used to gather performance data and factors that time out of its analysis. This allows you to get close to real world timings for every class in your code. So there is no need to litter your code with System.currentTimeMillis()

14. Profiler4J Profiler4J is a free open-source tool for profiling in Java. It is enabled by passing an argument at start-up with a path to the Profiler4J .jar file. It comes with several graphs and charts showing a call graph with method details, a call tree, a memory monitor, a class list and thread monitoring.

15. Java-Monitor Java-Monitor is a commercial tool with a free trial and installation is simple, just involving the dropping in of a WAR file. For example, for Tomcat copy the WAR file into tomcatinstalldirectory/ webapps. This tool monitors JVM memory, database connection and servlet performance.



Performance Testing with JMeter – Second Edition #performance #testing #with #jmeter


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Performance Testing with JMeter – Second Edition

Performance Testing with JMeter – Second Edition

What You Will Learn

  • Set up and prepare Apache JMeter for testing
  • Record test scenarios or create them from scratch
  • Test RESTful web services
  • Effectively monitor resources during performance tests
  • Build realistic, comprehensive, and maintainable test plans
  • Understand distributed testing using Vagrant, AWS, Flood.io, and BlazeMeter
  • Extend JMeter functionality through plugins
  • Understand and effectively use JMeter components to achieve testing needs

Authors

Bayo Erinle

Bayo Erinle is an author and senior software engineer with over 11 years of experience in designing, developing, testing, and architecting software. He has worked in various spectrums of the IT field, including government, commercial, finance, and health care. As a result, he has been involved in the planning, development, implementation, integration, and testing of numerous applications, including multi-tiered, standalone, distributed, and cloud-based applications. He is passionate about programming, performance, scalability, and all things technical. He is always intrigued by new technology and enjoys learning new things.

He currently resides in Maryland, US, and when he is not hacking away at some new technology, he enjoys spending time with his wife, Nimota, and their three children, Mayowa, Durotimi, and Fisayo.

He also authored Performance Testing with JMeter 2.9 (first edition and second edition) and JMeter Cookbook, both by Packt Publishing.

Table of Contents

We understand your time is important. Uniquely amongst the major publishers, we seek to develop and publish the broadest range of learning and information products on each technology. Every Packt product delivers a specific learning pathway, broadly defined by the Series type. This structured approach enables you to select the pathway which best suits your knowledge level, learning style and task objectives.

Learning

As a new user, these step-by-step tutorial guides will give you all the practical skills necessary to become competent and efficient.

Beginner’s Guide

Friendly, informal tutorials that provide a practical introduction using examples, activities, and challenges.

Essentials

Fast paced, concentrated introductions showing the quickest way to put the tool to work in the real world.

Cookbook

A collection of practical self-contained recipes that all users of the technology will find useful for building more powerful and reliable systems.

Blueprints

Guides you through the most common types of project you’ll encounter, giving you end-to-end guidance on how to build your specific solution quickly and reliably.

Mastering

Take your skills to the next level with advanced tutorials that will give you confidence to master the tool’s most powerful features.

Starting

Accessible to readers adopting the topic, these titles get you into the tool or technology so that you can become an effective user.



Northstar Performance – SureGrip Cylinder Head Stud Kits #head #stud #kit,northstar #thread

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The Northstar Engine has long been deemed the worst head gasket repair job in history by most mechanics and do-it-yourselfers. It’s not that the engine is that terribly hard to work on; but it’s because (until we introduced our repair method) the repairs never seemed to last.

The reason is because the threads inside the aluminum engine block corrode away and there’s nothing left for the head bolts to grab on to. A new gasket and clean surfaces is great! But if you can’t properly torque the bolts, all of that hard work and time goes to waste and you will have the same problem again. And believe me, it doesn’t matter how good those threads look or seem to be, you cannot trust them. All 20 cylinder head bolt holes MUST be repaired in one way or another. You do NOT want to tear the car apart again anytime soon.

For many years the repairs were being made with threaded sleeves/inserts that essentially make the bolt bigger, but they kept the same fine-thread on the insert that was causing the problems to begin with, so even those inserts were failing again and again. I don’t know about you; but I’m the kind of guy who believes that if something is worth doing, it’s worth doing right.

What I invented, was a dual-diameter cylinder head stud (US Patent #8,740,532) that has a very coarse thread and large diameter on one end to grab into the new threads you will be making in the engine block, and the remainder of the stud has been necked down to allow the cylinder head to slide over the studs, and a very nice fine-thread on the other end to allow for even torque of the fastener with hardened washers and nuts. Essentially, this means fixing the original problem by making it STRONGER than it ever was, not weaker by using inserts.

After repairing about 150 Cadillacs in our shop with this method, our very first commercially available Sure Grip Cylinder Head Stud Kit was sold to a customer in Salt Lake City, Utah about 5 years ago. We have since sold around 3500 sets to repair shops, Cadillac dealerships, and do-it-yourselfers across the USA, Canada, and as far away as Germany, Sweden, Iceland, and New Zealand. Our customers have had the same success we have had, which is a 100% rate of success. Our oldest customers still report to us from time to time and let us know the mileage they’ve racked up on their Northstar car!

Some of the Northstar engines that were remanufactured for General Motors, have our studs already inside. 48 of them to be exact. GM had outsourced the remanufacturing and that company came to us for the solution.

For the technical guys out there; the studs are machined at 3000 RPM in our Miyano CNC turning center from cold-rolled, American made chromoly steel. The studs are quenched and tempered and the threads are rolled after the heat treatment making for very strong threads. All of this boils down to a very, very durable repair.

They say peace-of-mind is worth a lot. Every customer I send a customer home from my shop with these studs in the engine, I tell them this: “Try to blow your head gaskets again. You can run your car as hard as you want to; and you won’t be able to”. I say this because I know it’s being fixed right. That means peace-of-mind to go across the country, from New York to Los Angeles, or to the deserts of New Mexico. Peace of mind to climb the hills in Tennessee, without worrying about blowing the head gaskets again.

Whether you’re the “spirited” driver who gives your car the odd burst of throttle to 100MPH, the Sunday driver who takes your Cadillac to church and gatherings, or the weekend racer who frequents the 1/4 mile drag strip- our solution was designed with you in mind.

Repair shops: the biggest thing I want to stress to you is this. Warranty. YES! You can offer your customers warranty now without even thinking that your customer will be back 2 months or a year later with the same overheating issue. That equates to a happy customer, a problem solved, and money in the bank. We can even advertise you as an installer and help customers in need, find you.

To order, select your kit below by determining the year of your Northstar powered car, and click add to cart. You can also call us direct at 888-800-9470. Leave a message if you don’t get an answer, we return calls promptly. If we don’t answer it just means we’re wrenching away on a Cadillac, or packaging stud kits.



High performance computing on graphics processing units #high #performance #computation


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We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal alignment of huge DNA se- quences in multi-GPU platforms using the exact Smith-Waterman method. Speed-up factors and energy consumption are monitored on different stages of the algorithm with the goal of identifying advantageous sce- narios to maximize acceleration [ ]

Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker [ ]

General purpose GPU (GPGPU) computing in virtualized environments leverages PCI passthrough to achieve GPU performance comparable to bare-metal execution. However, GPU passthrough prevents service administrators from performing virtual machine migration between physical hosts. Crane is a new technique for virtualizing OpenCL-based GPGPU computing that achieves within 5.25% of passthrough GPU performance while supporting VM migration. [ ]

In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices. RNN models are widely used for Natural Language Processing, Machine Translation, and other tasks. However, existing mobile applications that use RNN models do so on the cloud. To address privacy and efficiency concerns, we show how RNN models [ ]

CELES is a freely available MATLAB toolbox to simulate light scattering by many spherical particles. Aiming at high computational performance, CELES leverages block-diagonal preconditioning, a lookup-table approach to evaluate costly functions and massively parallel execution on NVIDIA graphics processing units using the CUDA computing platform. The combination of these techniques allows to efficiently address large [ ]

Due to increased demand for computational efficiency for the training, validation and testing of artificial neural networks, many open source software frameworks have emerged. Almost exclusively GPU programming model of choice in such software frameworks is CUDA. Symptomatic is also lack of the support for complex-valued neural networks. With our research going exactly in that [ ]

Reaching the so-called performance wall in 2004 inspired innovative approaches to performance improvement. Parallel programming, distributive computing, and System on a Chip (SOC) design drove change. Hardware acceleration in mainstream computing systems brought significant improvement in the performance of applications targeted directly to a specific hardware platform. Targeting a single hardware platform, however, typically requires [ ]

The number of heterogeneous components on a System-on-Chip (SoC) has continued to increase. Software developers leverage these heterogeneous systems by using high-level languages to enable the execution of applications. For the application to execute correctly, hardware support for features and constructs of the programming model need to be incorporated into the system. OpenCL is a [ ]

Traditional speedup models, such as Amdahl s, facilitate the study of the impact of running parallel workloads on manycore systems. However, these models are typically based on software characteristics, assuming ideal hardware behaviors. As such, the applicability of these models for energy and/or performance-driven system optimization is limited by two factors. Firstly, speedup cannot be measured [ ]

Multi-start algorithms are a common and effective tool for metaheuristic searches. In this paper we amplify multi-start capabilities by employing the parallel processing power of the graphics processer unit (GPU) to quickly generate a diverse starting set of solutions for the Unconstrained Binary Quadratic Optimization Problem which are evaluated and used to implement screening methods [ ]

The compact descriptors for visual search (CDVS) standard from ISO/IEC moving pictures experts group (MPEG) has succeeded in enabling the interoperability for efficient and effective image retrieval by standardizing the bitstream syntax of compact feature descriptors. However, the intensive computation of CDVS encoder unfortunately hinders its widely deployment in industry for large-scale visual search. In [ ]

Important computational physics problems are often large-scale in nature, and it is highly desirable to have robust and high performing computational frameworks that can quickly address these problems. However, it is no trivial task to determine whether a computational framework is performing efficiently or is scalable. The aim of this paper is to present various [ ]



Materials Planner I Salaries by education, experience, location and more #financial #planner

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Materials Planner I Salaries

Alternate Job Titles: Entry Level Materials Planner, Level I Materials Planner, Materials Planner I


Educational Leadership and School Improvement: Faculty of Education #pedal, #cambridge #university #drama,

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Educational Leadership and School Improvement

This route provides a broad-based view of educational leadership and school improvement, both through the explicit and focused study of specific concepts and issues, and through their application in the conduct of individual research projects. The teaching team draw on their research to illustrate ideas, and occasionally welcome visiting academics to enrich the route still further. Students are encouraged to share their experiences and perceptions, and to learn from each other while relating knowledge, principles and insights to their own contexts. Participants come with varied backgrounds, from the UK and overseas and as such the course has an international perspective.

We aim to offer participants the opportunity to develop:

  • an advanced knowledge and understanding of educational leadership and school improvement, including the ‘Leadership for Learning’ framework;
  • a familiarity with a range of frameworks for understanding pupil, professional and organisational learning;
  • a set of skills for analysing educational leadership and school improvement issues and practices;
  • the ability to understand and contribute to the leadership of educational initiatives and contribute to informed development of policy and practice in educational contexts.

What does this course offer?

The opportunities for learning on the ELSI route are designed to reflect our conceptions of learning and leadership and the principles and values of ‘Leadership for Learning’. This means that whilst the Faculty lecturers take responsibility for the organisation of the route and for supporting you, there is an expectation that you take responsibility for your own learning, and both contribute to and learn from other members of the group. Students come from different backgrounds and with a variety of experiences, which adds a real richness to the group: we aim to draw upon your own knowledge and experiences and encourage you to share these sensitively with others. As a community of learners we are interdependent.

How is the course organised?

Students on the MPhil course complete the course in one year and have teaching sessions throughout the week. Students on the MEd course complete the course part time over two years and have one teaching session per week on a Wednesday afternoon into early evening.

During sessions you will experience a variety of face-to-face activities offering opportunities for learning. For example, there may be a lecture to the whole group, discussions, small group activities, and student presentations. You will also be expected to engage in self-directed work and study, sometimes with other students in small groups, and sometimes on your own. You will be encouraged to develop critical friendship groups with fellow students, and to both give and gain support through these groups. These will complement your one-to-one supervisions with a lecturer that focus upon your assessed assignments.

The content is covered through eight interrelated themes (described below). In 2016-17, full time students will follow all 8 themes. Part time students will study themes 5-8 in 2016-17, and those continuing to their second year in 2017-18 will study themes 1-4.

Module 1: Leadership for Learning
Module 2: Policy, Structures and Change
Module 3: School Effectiveness and School Improvement
Module 4: Issues and Dilemmas
Module 5: Perspectives on Leadership
Module 6: Perspectives on Learning
Module 7: Schools, Cultures and Communities
Module 8: Educational Evaluation

Research Method Strand

All of our Masters degrees are designated research degrees in education. This means that a key part of the degree involves developing a good understanding of a wide range of empirical and non-empirical research methods (including techniques for collecting and analysing qualitative and quantitative data) and then applying these research methods to practical issues in education. Students develop an understanding of different research strategies, foster skills in appraising and synthesising published research studies and acquire the understanding and skills necessary for designing, conducting, analysing, interpreting and reporting a small-scale research study. Discussion of educational research methodology is integral to the ELSI sessions, and the second essay and thesis explicitly assess knowledge and understanding of research methods.

All students on the ELSI route also attend a generic research methods strand, accounting for approximately one-third of the whole programme. Methods sessions are essential for a research-based Masters degree and constitute about one-third of the whole programme. The research methods strand covers a broad range of social science research methods and is essential for Masters level understanding and critical engagement with the research literature. It offers opportunities and encouragement to apply the knowledge gained to your thematic area, and vice versa, as well as introducing research methods beyond those commonly used in ELSI.

Who are the course team?

The course is staffed by a team of established faculty members who provide teaching and supervision. Other colleagues also contribute one-off lectures:



Replacement Jeep Parts, Jeep Accessories, Bumpers, Soft Tops, leading Manufacture Omix-ADA #jeep,

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Omix-ADA, Inc.

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JEEP HITCHES

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