Persistent freak-4 Ways to Get the From a Dangerous Emotion | Leadership Freak

The year-old Londoner also thinks X Factor producers should give contestants training so they can spot if an obsessive fans crosses the line into stalker territory. It is a bit weird. Follow me! And you feel special! But then you realise they have done it to everyone else as well.

Persistent freak

Persistent freak

Persistent freak

How do Persistent freak approach Persistent freak challenge? If nothing happens, download the GitHub extension for Visual Studio and try again. Personal development is the result of action, not simply contemplation. That makes sense. Now how do you use it in your program? Placing these fences correctly can be a difficult task that even expert Pesristent will occasionally get wrong. However, not all steps impose a strong ordering among each other.

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How might problems and distress be the tools of authenticity? If I Persistent freak at it as a problem that just gets Perisstent the way feeak my mind and body wrapping itself around the challenge. While the ISP has previously confirmed that persistent pirates could be terminated, it has never publicly spelled out its policy in such detail. Paul Thornton Persisten July 24, at pm said:. Based on a work at leadershipfreak. Problems are challenges. Fat dick lyrics was not sent - check your email addresses! Join the conversation: Cancel reply Enter your comment here Seasoned leaders have the wisdom to know when to hold the line and when to be flexible. Can I handle the challenge or do I need to punt off to someone else who has a better skill set for that challenge? You are commenting using your WordPress. Learn how your comment data is processed. What benefits do persistent problems offer? Pick your random or favourite series from Persistent freak list below. P Paizuri Cheerleader vs.

But stubbornness refuses to consider alternatives.

  • Dating back to the turn of the last century, copyright holders have alerted Internet providers about alleged copyright infringers on their network.
  • An open mind reflects the potential of a new future.

But stubbornness refuses to consider alternatives. Stubbornness makes decisiveness a disaster. But success requires persistence. My observation is that decisiveness and stubbornness often live together. Stubbornness promotes ignorance. Stubborn leaders refuse to consider alternatives because an alternative might require change. Stubbornness motivates people to stop trying. Persistent leaders inspire people. Stubborn leaders de-motivate teams.

Why bother if the boss never changes her mind. Stubbornness alienates the best and brightest. Stubborn leaders shoot down suggestions and ideas. The best and brightest go somewhere else. Ask a trusted colleague when they see stubbornness in you. Ask when they see it. Explore suggestions. Put strong people on your team. Stubborn leaders end up with teams of pushovers. Develop backup plans with your team before you begin. Flexibility has a downside too. When you frequently change course you devalue dedication and hard work.

Single-mindedness is the strength to press through obstacles, disappointment, and resistance. Self-awareness is so important! Everyday we face situations that require us to find the appropriate spot to be on a continuum of possible behaviors. Each situation is different and requires a situation-specific response. Consider—Firm versus Flexible There are times to be firm and there are times to be flexible. The overly flexible leader is unwilling to take a firm stand.

They are wishy-washy and often flip flop on their position. On the other hand, the overly firm leader is rigid and sees every issue as black and white. Seasoned leaders have the wisdom to know when to hold the line and when to be flexible. I like its important to have an approach so that your team may have an idea of how you would view an issue but stubbornness is the red tape that often makes work unbearable and I personally have left a position because of this stance from my manager. Great content as usual Stubbornness is particularly dangerous when you fail to see changes, and shifts and adapt to them.

Want to insure your bottom line takes a negative hit? Practice Hubris! You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. This site uses Akismet to reduce spam. Learn how your comment data is processed.

Subscribe via email. Based on a work at leadershipfreak. Leadership Freak. Thanks for sharing. Jim Leemann on July 24, at pm said:. Paul Thornton on July 24, at pm said:.

DonaldFondelTulane on July 24, at pm said:. The Thinker on July 24, at pm said:. Join the conversation: Cancel reply Enter your comment here Fill in your details below or click an icon to log in:. Email required Address never made public. Name required. Join over , Leadership Freak followers, fans, and subscribers. Search for:. Blog at WordPress. Post to Cancel. Post was not sent - check your email addresses! Sorry, your blog cannot share posts by email.

People in leadership positions do not get to cherry pick which problems will surface in real-life. They see the search giant as responsible for piracy but doing nothing. F Facials of Eve 1 Fault!! Ask a trusted colleague when they see stubbornness in you. What on earth is going on if it receives 50 million requests to take down links to illegal sites?

Persistent freak

Persistent freak

Persistent freak. Ten years in jail Internet piracy


Succeeding with the Thin Line Between Stubborn and Persistent | Leadership Freak

When adapting or creating new applications that use Persistent Memory PM , developers are faced with two main choices: using a PTM or manually placing flushes and fences. If they chose hand-placed fences, they have to worry about the correct placement of the clwbs, sfences and, in which situations are non-temporal stores a better choice performance-wise.

Placing these fences correctly can be a difficult task that even expert researchers will occasionally get wrong. One incorrect fence or flush is enough the prevent the recovery of the application in the event of a crash, thus losing or even corrupting data in PM permanently.

Finding expert developers that are capable of dealing with durability and concurrency is likely the reason why database engines are a costly piece of software that requires a team of experts to assemble. If the application developer chooses instead to use a PTM, concerns of how to make the code durable and concurrent, disappear. The developer's task now becomes the identification of which blocks of code and data must be accessed in an atomic way, and encapsulate those inside a transaction.

Notice that identifying atomicity of data is a task required even when choosing the hand-placement of flushes and fences. For the end-developer, the code within a transaction block can be reasoned about as if it were sequential. This shift of complexity from the application developer to the PTM library implementer, tremendously increases development speed for the application developer, reduces bug count and improves code maintainability.

Monday, October 14, What is a concurrent algorithm without order? I've heard Leslie Lamport mention on multiple occasions: " An algorithm is not a program!

Code is bounded by the constructs that form the language in which that code is written. An algorithm has no such constraints and is the mathematical expression of an idea. An algorithm is precise, yet, universal.

A program works only for a particular language. When an algorithm is incorrect, a program that uses such an algorithm will be incorrect as well. When an algorithm is correct, a program that uses such an algorithm may be incorrect because it implemented the algorithm incorrectly, but the correctness of the underlying algorithm in unaffected.

An algorithm has mathematical beauty. A well made program can have craftsmanship and be appreciated as beautiful, but rarely if ever on the mathematical sense. So what do I mean by " An algorithm without ordering is not an algorithm "? In practice, if you were to implement a concurrent algorithm in such a way, it would likely be slow because each store and load on non-TSO CPUs would require a fence to guarantee ordering.

Researchers writing papers assume this strong ordering because they focus on " the algorithm " as being a series of steps and leave the dependencies of those steps ordering as an implementation detail. The amount of fences are not always, but usually what dictates the throughput of a concurrent algorithm. This means that the placement and number of fences ordering constraints are vital to the algorithm, not just for correctness but also for performance reasons.

The same logic applies to durable algorithms, where the ordering constraints are typically the main performance indicator. A durable algorithm without ordering does not guarantee durability and therefore, it becomes useless. However, not all steps impose a strong ordering among each other.

It becomes important when we describe the algorithm to explicitly mention this dependency of steps. And this is one of the main issues of concurrent algorithms and even distributed systems.

Going back to Leslie, he was the first to show that to have mutual exclusion on a concurrent system we need to have at least a store-load fence. In other words, there is a minimum amount of ordering that is needed to make an algorithm that is mutually exclusive. And the reason behind it is somewhat related to ordering. Likely this happens because our human brains are so used to thinking in sequential order that we expect the steps code that we write in a certain order to be executed in that exact order, because that's how the physical world around us typically behaves.

The problem is, concurrent algorithms don't follow these rules and because of that, ordering must be part of a concurrent algorithm. IMO, a concurrent algorithm without a specification of the order is an incomplete algorithm. Same thing for durable algorithms. Labels: atomics , concurrency control algorithm , Durability , mutual exclusion.

Sunday, October 6, Recovery on a hand-made durable data structure versus recovery on a Persistent Transactional Memory. These data structures have to be resilient to failures. In other words, if there is a power cut during an operation on the data structure, the data structure must be correctly recovered after the system restarts.

For the rest of this post, and likely for all other posts you read in this site, when I say " durable data structure " I mean a data structure that has "atomic durability": In the event of a failure, the side effects of all completed operations will be visible upon restart and recovery.

The are two main approaches to having a durable data structure in PM: 1 Write an algorithm for a new data structure. This algorithm has to be failure-resilient; 2 Write a regular data structure and wrap it in a PTM; Making a new data structure is typically the subject of an entire research paper, while using a PTM is not. Which approach is better depends on what you mean by "better", but my favorite is 2. However, set's say you decide to use a hand-made durable data structure that someone else made.

Now how do you use it in your program? For an application that needs durability or persistence, if you prefer not all data needs to be persistent, which means that not all data structures need to be persistent either. This means we need to call the recovery method for each of these data structure instances, just in case. In PM, when you corrupt the data it's gone forever Another difficulty is that sometimes the number of durable data structures may be dynamic because the application creates and destroys data structures during execution, to store some data.

This means that it's not possible to add each data structure instance in the recovery method because they may not exist when the program restarts. This typically means you will need a registration mechanism where newly created data structure instances are added and later de-registered when the data structure is destroyed. Also, you need to save the root pointer to each of those data structure instances. PTMs handle this transparently because the transaction has the all-or-nothing semantics.

When there is a crash, any modifications to ongoing transactions will be reverted, including allocating or de-allocating a new data structure instance and including adding or removing a root pointer. Ok, ok, so it's not that bad for the hand-made durable data structures. We can do automatic checks for these scenarios by adding registration mechanisms in the constructor of the data structure, or even some kind of static check or compile-time assertion to make sure everything is as it should be.

But the thing is, if you use a PTM, you only have to call the recovery method of the PTM, one time , regardless of the number of data structure instances you have in your application! In the case of OneFile, it has null recovery which means there is no recovery method: the PTM will simply continue execution where it left of. This is yet another thing where PTMs are better than an hand-made durable data structures. Tuesday, October 1, Atomic Durability - How do databases recover from a crash?

In this post we're going to talk about the four different ways of having durable transactions. If you want to know how databases and file systems guarantee correct data recovery after a power failure, then keep reading!

If there is a failure half-way through, you may end up with corrupted data. Suppose you're changing a customer address in a database. The current address is " Bag End, Shire " and we want to change it to " Rivendell ". What happens of there is a crash half-way through the write?

Suppose the first four bytes were written before the crash occurred. Upon restart, the address in the database is now " Rive nd, Shire " What should we do? Generally speaking, there are four solutions to this problem and they are: 1 Undo-Log; 2 Redo-Log; 3 Copy-On-Write sometimes called Shadow Copy or Shadow Paging ; 4 Romulus; Before we explain how each of these works, we need to introduce two things which we're going to call " ordering fence " and " synchronization fence ".

An "ordering fence" is something that guarantees that previous writes will reach the durable media before subsequent writes. A "synchronization fence" is something that guarantees that previously written data is now durable.

And by durable I mean it's persistent in the media and in the event of a crash, this data will not be lost. As the name indicates, it's an undo-log technique. This is the library in PMDK that provides durable transactions. Let's examine what happens if there is a crash in: - Lines 1 to 4: recovery does nothing; - Lines 4 to 8: recovery method will re-apply the contents of the log to the data redo overwriting any old values that may have been left incomplete in case the crash occurred during line 6 ; - After line 8: recovery does nothing; Above, we saw the algorithm for a single write.

No matter how many write s are done in the transaction, it's always 4 fences. Without it, we would be reading stale data and break invariants. In the context of Persistent Memory, redo-log is what Mnemosyne and Onefile do. This technique doesn't allow for transactional semantics. For that, it needs to be coupled with either undo-log or redo-log. The COW technique is always applied to a block or an object. In the context of Persistent Memory, there is no PTM that uses COW but there are hand-made data structures that use this technique extensively, examples being the ones described in the MOD paper although their algorithm requires two fences per operation to be correct, plus whatever the allocator does.

No matter how many write s are done in the transaction. The volatile log can grow dynamically and if it grows too much, we can just stop using it and copy the entire 'main' to 'back' at the end of the transaction. Each of them has different trade-offs in terms of performance, memory usage, ease-of-implementing and ease-of-use. In the end, it's an engineering choice which of these four algorithms is best suited for a particular application.

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Persistent freak

Persistent freak