Cache Memory Quotes

Who was I, really? I was the sole occupant of my mother's totalitarian state, my own personal history rewritten to fit the story she was telling that day. There were so many missing pieces. I was starting to find some of them, working my way upriver, collecting a secret cache of broken memories in a shoebox.

Writing about oneself is an egotistical adventure unless the act of self-exploration revolves around the distinct goal of heightening a person’s cache of knowledge, ideas, and level of self-awareness.

When a field is declared volatile, the compiler and runtime are put on notice that this variable is shared and that operations on it should not be reordered with other memory operations. Volatile variables are not cached in registers or in caches where they are hidden from other processors, so a read of a volatile variable always returns the most recent write by any thread.

The truth is you already know what it’s like. You already know the difference between the size and speed of everything that flashes through you and the tiny inadequate bit of it all you can ever let anyone know. As though inside you is this enormous room full of what seems like everything in the whole universe at one time or another and yet the only parts that get out have to somehow squeeze out through one of those tiny keyholes you see under the knob in older doors. As if we are all trying to see each other through these tiny keyholes. But it does have a knob, the door can open. But not in the way you think. But what if you could? Think for a second — what if all the infinitely dense and shifting worlds of stuff inside you every moment of your life turned out now to be somehow fully open and expressible afterward, after what you think of as you has died, because what if afterward now each moment itself is an infinite sea or span or passage of time in which to express it or convey it, and you don’t even need any organized English, you can as they say open the door and be in anyone else’s room in all your own multiform forms and ideas and facets? Because listen — we don’t have much time, here’s where Lily Cache slopes slightly down and the banks start getting steep, and you can just make out the outlines of the unlit sign for the farmstand that’s never open anymore, the last sign before the bridge — so listen: What exactly do you think you are? The millions and trillions of thoughts, memories, juxtapositions — even crazy ones like this, you’re thinking — that flash through your head and disappear? Some sum or remainder of these? Your history? Do you know how long it’s been since I told you I was a fraud? Do you remember you were looking at the respicem watch hanging from the rearview and seeing the time, 9:17? What are you looking at right now? Coincidence? What if no time has passed at all?* The truth is you’ve already heard this. That this is what it’s like. That it’s what makes room for the universes inside you, all the endless inbent fractals of connection and symphonies of different voices, the infinities you can never show another soul. And you think it makes you a fraud, the tiny fraction anyone else ever sees? Of course you’re a fraud, of course what people see is never you. And of course you know this, and of course you try to manage what part they see if you know it’s only a part. Who wouldn’t? It’s called free will, Sherlock. But at the same time it’s why it feels so good to break down and cry in front of others, or to laugh, or speak in tongues, or chant in Bengali — it’s not English anymore, it’s not getting squeezed through any hole. So cry all you want, I won’t tell anybody.

History is never something carved in stone, but more like something saved to a temporary cache file on a computer disk vulnerable to the imperfections of memory and always ready to be revised. The Confessions are Augustine’s own first draft of history.

The memory feats of food-caching animals that can remember the location of hundreds of food stores are highly similar to the ability of some people with autism to memorize every street in a city. My theory is that savant-type skills occur when memories are sensory-based instead of language-based. Language leads to abstractification and loss of detail. Animals naturally lack language and autistic people have language problems because of a disorder, but in autistic people and animals the cause of sensory-based memory is the same: thinking and remembering in pictures instead of words.

When a process is rescheduled to run on a multiprocessor system, it doesn’t necessarily run on the same CPU on which it last executed. The usual reason it may run on another CPU is that the original CPU is already busy. When a process changes CPUs, there is a performance impact: in order for a line of the process’s data to be loaded into the cache of the new CPU, it must first be invalidated (i.e., either discarded if it is unmodified, or flushed to main memory if it was modified), if present in the cache of the old CPU. (To prevent cache inconsistencies, multiprocessor architectures allow data to be kept in only one CPU cache at a time.) This invalidation costs execution time. Because of this performance impact, the Linux (2.6) kernel tries to ensure soft CPU affinity for a process — wherever possible, the process is rescheduled to run on the same CPU.

I’ve sat at the piano for hours already, looking for lyrics and melodies, but everything sounds the same and I feel as uninspired as ever. Does it mean I’m finished? A more sobering thought: if I’m finished, would I miss it? But the truth is, I’ve been here before. Many times. We all have. So how do we find the faith to press on? Remember. Remember, Hebrew children, who you once were in Egypt. Remember the altars set up along the way to remind yourselves that you made the journey and God rescued you from sword and famine, from chariots and pestilence, that once you were there, but now you are here. It happened. Our memories are fallible, residing in that most complex and mysterious organ in the human body (and therefore the known universe), capable of being suppressed, manipulated, altered, but also profoundly powerful and able to transport a person to a place fifty years ago all because of a whiff of your grandfather’s cologne or an old book or the salty air. As often as you do this, do it in remembrance of me. Remember with every sip of wine that we shared this meal, you and I. Remember. So I look at the last album, the last book, and am forced to admit that I didn’t know anymore then than I do now. Every song is an Ebenezer stone, evidence of God’s faithfulness. I just need to remember. Trust is crucial. So is self-forgetfulness and risk and a measure of audacity. And now that I think about it, there’s also wonder, insight, familiarity with Scripture, passion, a good night’s sleep, breakfast (preferably an egg sandwich), an encouraging voice, diligence, patience. I need silence. Privacy. Time—that’s what I need: more time. But first I need a vacation, because I’ve been really grinding away at this other stuff and my mental cache is full. A deadline would be great. I work best with deadlines, and maybe some bills piling up. Some new guitar strings would help, and a nice candle. And that’s all I need, in the words of Steve Martin’s The Jerk. This is the truth: all I really need is a guitar, some paper, and discipline. If only I would apply myself.

For server-side caching, you can easily use Django’s cache utilities. This will trade memory usage to store the cached values while saving the CPU cycles required to generate the images, as

ZODB, for example, is similar to Python’s shelve but addresses many of its limitations, better supporting larger databases, concurrent updates, transaction processing, and automatic write-through on in-memory changes (shelves can cache objects and flush to disk at close time with their writeback option, but this has limitations: see other resources).

Writers’ never-ending quest involves investigating genuineness while carving out narrative nonfiction. They must strive to reach great truths by recounting untold lies with acute enthusiasm. Culmination of a sprawling personal saga is an attempt to flesh out from the ichors of a person’s reptilian instincts and mammalian brain patterns the epicene embodiment of the originator’s dream works intermingled with their actual remembered sensory observations. One unleashes their cache of blood-tinged memories along with an X-ray beam of reminiscent enlightenment to forge a flowing stream of self-consciousness dedicated to the task of hunting out a new way of perceiving, thinking, and communicating.

One and a half million Jewish men and women and children: How was anyone to understand a number like that? Andras knew it took three thousand to fill the seats of the Dohány Street Synagogue. To accommodate a million and a half, one would have had to replicate that building, its arches and domes, its Moorish interior, its balcony, its dark wooden pews and gilded art, five hundred times. And then to envision each of those five hundred synagogues filled to capacity, to envision each man and woman and child inside as a unique and irreplaceable human being, the way he imagined Mendel Horovitz or the Ivory Tower or his brother Mátyás, each of them with desires and fears, a mother and a father, a birthplace, a bed, a first love, a web of memories, a cache of secrets, a skin, a heart, an infinitely complicated brain - to imagine them that way, and then to imagine them dead, extinguished for all time - how could anyone begin to grasp it?

In addition to the size of the scheduler cell, this approach might also limit the amount of cache as well as the memory bandwidth on multi-core processors with shared cache.

multithreaded programs are subject to all the performance hazards of single-threaded programs, and to others as well that are introduced by the use of threads. In well designed concurrent applications the use of threads is a net performance gain, but threads nevertheless carry some degree of runtime overhead. Context switches—when the scheduler suspends the active thread temporarily so another thread can run—are more frequent in applications with many threads, and have significant costs: saving and restoring execution context, loss of locality, and CPU time spent scheduling threads instead of running them. When threads share data, they must use synchronization mechanisms that can inhibit compiler optimizations, flush or invalidate memory caches, and create synchronization traffic on the shared memory bus. All these factors introduce additional performance costs;

An object is immutable if: Its state cannot be modifled after construction; All its flelds are final;[12] and [12] It is technically possible to have an immutable object without all fields being final—String is such a class—but this relies on delicate reasoning about benign data races that requires a deep understanding of the Java Memory Model. (For the curious: String lazily computes the hash code the first time hashCode is called and caches it in a nonfinal field, but this works only because that field can take on only one nondefault value that is the same every time it is computed because it is derived deterministically from immutable state. Don't try this at home.) It is properly constructed (the this reference does not escape during construction).

For each input source, Spark Streaming launches receivers, which are tasks running within the application’s executors that collect data from the input source and save it as RDDs. These receive the input data and replicate it (by default) to another executor for fault tolerance. This data is stored in the memory of the executors in the same way as cached RDDs.1

It is worth noting that performing CPU and memory profiling on your code will probably start you thinking about higher-level algorithmic optimizations that you might apply. These algorithmic changes (e.g., additional logic to avoid computations or caching to avoid recalculation) could help you avoid doing unnecessary work in your code, and Python’s expressivity helps you to spot these algorithmic opportunities.

Another benefit of the static nature of tuples is something Python does in the background: resource caching. Python is garbage collected, which means that when a variable isn’t used anymore Python frees the memory used by that variable, giving it back to the operating system for use in other applications (or for other variables). For tuples of sizes 1–20, however, when they are no longer in use the space isn’t immediately given back to the system, but rather saved for future use. This means that when a new tuple of that size is needed in the future, we don’t need to communicate with the operating system to find a region in memory to put the data, since we have a reserve of free memory already. While this may seem like a small benefit, it is one of the fantastic things about tuples: they can be created easily and quickly since they can avoid communications with the operating system, which can cost your program quite a bit of time.

One important lesson to take away from this is that you should always take care of any administrative things the code must do during initialization. This may include allocating memory, or reading configuration from a file, or even precomputing some values that will be needed throughout the lifetime of the program. This is important for two reasons. First, you are reducing the total number of times these tasks must be done by doing them once up front, and you know that you will be able to use those resources without too much penalty in the future. Secondly, you are not disrupting the flow of the program; this allows it to pipeline more efficiently and keep the caches filled with more pertinent data. We also learned more about the importance of data locality and how important simply getting data to the CPU is. CPU caches can be quite complicated, and often it is best to allow the various mechanisms designed to optimize them take care of the issue. However, understanding what is happening and doing all that is possible to optimize how memory is handled can make all the difference. For example, by understanding how caches work we are able to understand that the decrease in performance that leads to a saturated speedup no matter the grid size in Figure 6-4 can probably be attributed to the L3 cache being filled up by our grid. When this happens, we stop benefiting from the tiered memory approach to solving the Von Neumann bottleneck.

In order to optimize the memory-dominated effects, let us try using the same method we used in Example 6-6 in order to reduce the number of allocations we make in our numpy code. Allocations are quite a bit worse than the cache misses we discussed previously. Instead of simply having to find the right data in RAM when it is not found in the cache, an allocation also must make a request to the operating system for an available chunk of data and then reserve it. The request to the operating system generates quite a lot more overhead than simply filling a cache — while filling a cache miss is a hardware routine that is optimized on the motherboard, allocating memory requires talking to another process, the kernel, in order to complete. In order to remove the allocations in Example 6-9, we will preallocate some scratch space at the beginning of the code and then only use in-place operations. In-place operations, such as +=, *=, etc., reuse one of the inputs as their output. This means that we don’t need to allocate space to store the result of the calculation.

in the absence of synchronization, the Java Memory Model permits the compiler to reorder operations and cache values in registers, and permits CPUs to reorder operations and cache values in processor-specific caches.

The Java language also provides an alternative, weaker form of synchronization, volatile variables, to ensure that updates to a variable are propagated predictably to other threads. When a field is declared volatile, the compiler and runtime are put on notice that this variable is shared and that operations on it should not be reordered with other memory operations. Volatile variables are not cached in registers or in caches where they are hidden from other processors, so a read of a volatile variable always returns the most recent write by any thread. A

This is clearly not so. Pigeons, for example, do better than humans at mentally rotating visual images, and some birds have an amazing memory for the location of hidden objects. Clark's nutcrackers store up to 33,000 seeds in caches distributed over many square kilometers and find most of the caches again months later.28As someone who occasionally forgets where he has parked an item as large and significant as his car, I am impressed by these peanut-brained birds.

Memory is consumed by four things: the kernel, filesystem caches, processes, and intimately shared memory.

NCA uses a kernel module to transparently cache static web content in a kernel memory buffer, and replies to HTTP document requests for documents in its cache without ever waking up the application web server.

In the pantry, waiting to awaken memories of flavors of Georgia, is a cache of things carried back from trips over the years: jars of neon-red adjika spice paste, packets of savory Svan salt and small glass bottles of precious Kakhetian sunflower oil, glowing yellow as buttercups.

The advantage of subdividing the heap is that multiple threads can each allocate objects at the same time without interfering with one another. Further, by allocating object used by the same thread from the same memory region, cache hit rates may improve.

I have so much information stored up inside my brain that to add more I need to clean out my mind's cache once in awhile.

CPU affinity can lead to improved performance if the application has a larger cache footprint and can benefit from the additional cache offered by aggregating multiple pCPUs.

Antagonistic neighbors Other processes (often completely unrelated and run by different teams) can have a significant impact on the performance of your processes. We’ve seen differences in performance of this nature of up to 20%. This difference mostly stems from competition for shared resources, such as space in memory caches or bandwidth, in ways that may not be directly obvious.

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Logon and Service Access Summary Figure 10-5 summarizes the process we’ve discussed so far. To review, the following steps happen when a user logs onto his workstation and attempts to access a service: An AS_REQ message is sent to begin the authentication process. This message proves the user’s identity by encrypting a message with a hash of the user’s password. Using a hash ensures that the actual password is never transmitted over the network. The domain controller validates the request and produces a ticket granting ticket. The TGT is sent back in an AS_REP message. The client caches the TGT in memory and uses it for subsequent requests for service tickets. The client sends a TGS_REQ message to the DC to request a service ticket for a specific service. Rather than providing credentials again, the client sends the TGT that it cached in memory after step 2. The DC validates the TGS_REQ and constructs a service ticket for the service. The service ticket, encrypted with a hash of the services’s secret, is sent back to the client in a TGS_REP message. The client caches this ticket in memory for subsequent use when authenticating directly to the service. The client presents the service ticket to the service in an AP_REQ message. The service uses this to authenticate the user. The service might also use the user’s access token (contained in the ticket) to perform authorization before allowing access. Optionally, the service can respond with an AP_REQ message for mutual authentication of the service. This is not especially common and entirely optional.