Cpu Quotes

I say ‘Uhmm...’ a lot. I mentioned this to Karla and she says it’s a CPU word. It means you’re assembling data in your head - spooling.

Please disregard the previous disregard message. Sender undoubtedly has its CPU stuck up its posterior access port.

One more thing. If you ever threaten me again, make sure you’re ready for a fight. I might be a kid, I might even be a trust fund brat, but lady, I’m not a coward. I’m a CPU Legacy with a very bored mother who likes to start shit. I promise you, you don’t wanna mess with either

A modern CPU is just a circuit of millions of microscopic wires and logic gates that manipulate electric currents of information.

We have proposed a system for electronic transactions without relying on trust. We started with the usual framework of coins made from digital signatures, which provides strong control of ownership, but is incomplete without a way to prevent double-spending. To solve this, we proposed a peer-to-peer network using proof-of-work to record a public history of transactions that quickly becomes computationally impractical for an attacker to change if honest nodes control a majority of CPU power.

Stop for a second to behold the miracle of engineering that these hand-held, networked computers represent—the typical CPU in a modern smartphone is ten times more powerful than the Cray-1 supercomputer installed at Los Alamos National Laboratory in 1976.

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.

The finite interval 0-1 on the Number Line is thus even more inconceivably crowded. There's not only an infinite number of infinite sequences of fractions, but also an infinite number of surds, each of which is itself numerically inexpressible except as an infinite sequence of nonperiodic decimals. Let's pause to consider the vertiginous levels of abstraction involved here. If the human CPU cannot apprehend or even really conceive of (infinity)s, an infinite number of individual members of which are themselves not finitely expressible, all in an interval so finite- and innocent-looking we use it in little kids' classrooms. All of which is just resoundingly weird.

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.

VMware ESXi has the ability to detect the physical processor topology as well as the relationships among processor sockets, cores, and the logical processors on those cores. The scheduler then makes use of this information to optimize the placement of VM vCPUs.

the VMM might not always be

Any time spent by the VMM on behalf of the VM is excluded from the progress calculation.

At some point the skew will grow to exceed a threshold in milliseconds, and as a result, all of the vCPUs of the VM will be co-stopped and will be scheduled again only when there are enough physical CPUs available to schedule all vCPUs simultaneously.

Distributed Locking with the Scheduler Cell

A VM performs best when all its vCPUs are co-scheduled on distinct processors.

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.

The first is referred to as a pull migration, where an idle physical CPU initiates the migration. The second policy is referred to as a push migration, where the world becomes ready to be scheduled. These policies enable ESXi to achieve high CPU utilization and low latency scheduling.

VMware has given you as an administrator the ability to restrict the pCPUs that a VM's vCPUs can be scheduled on. This feature is called CPU scheduling affinity.

slowest vCPU and each of the other vCPUs.

only the vCPUs that advanced too much are individually stopped,

ESXi CPU scheduler has the responsibility to ensure that all vCPUs assigned to a VM are scheduled to execute on the physical processors in a synchronized manner so that the guest OS running inside the VM is able to meet this requirement for its threads or processes.

In other words, always allow the VM to be scheduled on at least one more pCPU than the number of vCPUs configured for the VM.

In addition, new multi-core processors make the problem even worse because multiple cores are now being starved for memory during memory access operations.

Indeed. While I haven’t been able to pinpoint our exact location, I’ve been able to cross-reference our origin point with an estimate of our heading. When I factor in our current velocity, something quite interesting happens.” “Oh?” said Mech. “What?” “My CPU catches fire,” Kevin replied. Mech’s eyes narrowed. “And how does that help us?” “Oh, it doesn’t, sir,” Kevin replied.

The fact that our Universe (together with the entire Level III multiverse) may be simulatable by a quite short computer program calls into question whether i makes any ontological difference whether simulations are "run" or not. If, as I have argued, the computer need only describe and not compute the history, then the complete description would probably fit on a single memory stick, and no CPU power would be required. It would appear absurd that the existence of this memory stick would have any impact whatsoever on whether the multiverse it describes exists "for real." Even if the existence of the memory stick mattered, some elements of this multiverse will contain an identical memory stick that would "recursively" support its own physical existence. This wouldn't involve any Catch-22, chicken-or-the-egg problem regarding whether the stick or the multiverse was created first, since the multiverse elements are four-dimensional spacetimes, whereas "creation" is of course only a meaningful notion within a spacetime. So are we simulated? According to the MUH, our physical reality is a mathematical structure, and as such, it exists regardless of whether someone here or elsewhere in the Level IV multiverse writes a computer program to simulate/describe it. The only remaining question is then whether a computer simulation could make our mathematical structure in any meaningful sense exist even more than it already did. If we solve the measure problem, perhaps we'll realize that simulating it would increase its measure slightly, by some fraction of the measure of the mathematical structure within which it's simulated. My guess is that this would be a tiny effect at best, so if asked, "Are we simulated?," I'd bet my money on "No!

The circuitry of a modern CPU is housed in a single integrated circuit or chip, millions of miniature circuits manufactured in a sliver of silicon. A processor's current instruction and data values are stored temporarily inside the CPU in special high-speed memory locations called registers. Some multiprocessor computers have multiple CPU chips or a multi-core processor (a single chip containing multiple CPUs). These computers are capable of faster speeds because they can process different sets of instructions at the same time.

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

Somebody needs a significantly powerful CPU;

each service can be deployed on hardware that’s best suited to its resource requirements. This is quite different than when using a monolithic architecture, where components with wildly different resource requirements—for example, CPU-intensive vs. memory-intensive—must be deployed together.

great creative work isn’t possible if you’re trying to piece together 30 minutes here and 45 minutes there. Large, uninterrupted blocks of time—3 to 5 hours minimum—create the space needed to find and connect the dots. And one block per week isn’t enough. There has to be enough slack in the system for multi-day, CPU-intensive synthesis.

Notably, the “clock rate” for modern information processors, such as the CPU of a high-end laptop, is approaching 10 gigahertz, corresponding to ten billion operations per second. Computers can work much faster than brains, because transistors use the electrically driven motion of electrons, instead of the much slower processes of diffusion and chemical change that neurons rely on. By this natural measure, the limiting speed of thought for artificial intelligence is roughly a billion times faster than the speed of thought for natural intelligence.

Since the 1980s, Intel has specialized in a type of chip called a CPU, a central processing unit, of which a microprocessor in a PC is one example. These are the chips that serve as the “brain” in a computer or data center. They are general-purpose workhorses, equally capable of opening a web browser or running Microsoft Excel. They can conduct many different types of calculations, which makes them versatile, but they do these calculations serially, one after another.

Where a CPU would feed an algorithm many pieces of data, one after the other, a GPU could process multiple pieces of data simultaneously. To learn to recognize images of cats, a CPU would process pixel after pixel, while a GPU could “look” at many pixels at once. So the time needed to train a computer to recognize cats decreased dramatically.

possible to run any AI algorithm on a general-purpose CPU, but the scale of computation required for AI makes using CPUs prohibitively

Intel has specialized in a type of chip called a CPU,

To learn to recognize images of cats, a CPU would process pixel after pixel, while a GPU could “look” at many pixels at once. So the time needed to train a computer to recognize cats decreased dramatically.

Every day you stay at CPU, your life will get worse.” I run my finger down her jaw, and a spark of pleasure goes through me when she jerks her head away. “If you leave, it all stops. But if you stay… I’m going to have so much fun tormenting you, sweetheart. Fucking count on that.

80386 instruction set supports interlocked increment and decrement, but the result of the increment/decrement operation is not returned. Only the flags are updated by the operation. As a result, the only information you get back from the CPU about the result of the operation is whether it was zero, positive, or negative. (Okay, you also get some obscure information such as whether there were an even or odd number of 1 bits in the result, but that’s hardly useful nowadays.)

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;

1.1M    ./scripts 58M     ./cloud9 74M     . You can also use tee to write the output to several files at the same time, as shown in this example: root@beaglebone:/opt# du ‐d1 ‐h | tee /tmp/1.txt /tmp/2.txt /tmp/3.txt Filter Commands (from sort to xargs) There are filtering commands, each of which provides a useful function: sort: This command has several options, including (‐r) sorts in reverse; (‐f) ignores case; (‐d) uses dictionary sorting, ignoring punctuation; (‐n) numeric sort; (‐b) ignores blank space; (‐i) ignores control characters; (‐u) displays duplicate lines only once; and (‐m) merges multiple inputs into a single output. wc (word count): This can be used to calculate the number of words, lines, or characters in a stream. For example: root@beaglebone:/tmp# wc < animals.txt  4  4 18 This has returned that there are 4 lines, 4 words, and 18 characters. You can select the values independently by using (‐l) for line count; (‐w) for word count; (‐m) for character count; and (‐c) for the byte count (which would also be 18 in this case). head: Displays the first lines of the input. This is useful if you have a very long file or stream of information and you want to examine only the first few lines. By default it will display the first 10 lines. You can specify the number of lines using the ‐n option. For example, to get the first five lines of output of the dmesg command (display message or driver message), which displays the message buffer of the kernel, you can use the following: root@beaglebone:/tmp# dmesg | head ‐n5   [    0.000000] Booting Linux on physical CPU 0x0   [    0.000000] Initializing cgroup subsys cpuset   [    0.000000] Initializing cgroup subsys cpu   [    0.000000] Initializing cgroup subsys cpuacct   [    0.000000] Linux version 3.13.4-bone5(root@imx6q-sabrelite-1gb-0) tail: This is just like head except that it displays the last lines of a file or stream. Using it in combination with dmesg provides useful output, as shown here: root@beaglebone:/tmp# dmesg | tail ‐n2   [   36.123251] libphy: 4a101000.mdio:00 - Link is Up - 100/Full   [   36.123421] IPv6:ADDRCONF(NETDEV_CHANGE): eth0:link becomes ready grep: A very powerful filter command that can parse lines using text and regular expressions. You can use this command to filter output with options, including (‐i) ignore case; (‐m 5) stop after five matches; (‐q) silent, will exit with return status 0 if any matches are found; (‐e) specify a pattern; (‐c) print a count of matches; (‐o) print only the matching text; and (‐l) list the filename of the file containing the match. For example, the following examines the dmesg output for the first three occurrences of the string “usb,” using ‐i to ignore case: root@beaglebone:/tmp# dmesg |grep ‐i ‐m3 usb   [    1.948582] usbcore: registered new interface driver usbfs   [    1.948637] usbcore: registered new interface driver hub   [    1.948795] usbcore: registered new device driver usb You can combine pipes together. For example, you get the exact same output by using head and displaying only the first three lines of the grep output: root@beaglebone:/tmp# dmesg |grep ‐i usb |head ‐n3   [    1.948582] usbcore: registered new interface driver usbfs   [    1.948637] usbcore: registered new interface driver hub   [    1.948795] usbcore: registered new device driver usb xargs: This is a very powerful filter command that enables you to construct an argument list that you use to call another command or tool. In the following example, a text file args.txt that contains three strings is used to create three new files. The output of cat is piped to xargs, where it passes the three strings as arguments to the touch command, creating three new files a.txt, b.txt,

you got?" "Garth'll show you if you get time to stop in Merritt. The load must've been pretty loose. Most of the cartons are all crushed and dented and look as if they rattled around and bounced off the walls all the way down. Those are mostly the larger cartons - computer monitors, CPU's, and cartons full of smaller boxes of parts. But there were two skids of smaller, flatter cartons, cartons containing

RAM sticks do get hot, especially if overclocked and need to be cooled. Though not as serous as other components like the CPU or GPU, your DIMMS still need cooling. 

The silicon microchips themselves might be cheap (relative to times past, anyway), but CPU cycles are not cheap. Every CPU cycle consumes clock time. Clock time is latency. A wasteful application makes its users wait longer than they need to, and if there’s anything users hate, it’s waiting. For web systems, latency in the application has a dual effect. The added processing directly increases the burden on the application servers themselves. Suppose that an application takes just 250 milliseconds of extra processing per transaction. If the system processes a million transactions a day, that extra 250 milliseconds per transaction makes for an extra 69.4 hours of compute time every day. Assuming an 80% load factor on each server, you’ll need four additional servers to handle this load.

One downfall of numpy’s optimization of vector operations is that it only occurs on one operation at a time. That is to say, when we are doing the operation A * B + C with numpy vectors, first the entire A * B operation completes, and the data is stored in a temporary vector; then this new vector is added with C. The in-place version of the diffusion code in Example 6-14 shows this quite explicitly. However, there are many modules that can help with this. numexpr is a module that can take an entire vector expression and compile it into very efficient code that is optimized to minimize cache misses and temporary space used. In addition, the expressions can utilize multiple CPU cores (see Chapter 9 for more information) and specialized instructions for Intel chips to maximize the speedup. It is very easy to change code to use numexpr: all that’s required is to rewrite the expressions as strings with references to local variables. The expressions are compiled behind the scenes (and cached so that calls to the same expression don’t incur the same cost of compilation) and run using optimized code. Example 6-19 shows the simplicity of changing the evolve function to use numexpr. In this case, we chose to use the out parameter of the evaluate function so that numexpr doesn’t allocate a new vector to which to return the result of the calculation.

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.

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.

   class Computer      def initialize(computer_id, data_source)        @id = computer_id        @data_source = data_source      end             »    def self.define_component(name) »      define_method(name) do »        info = @data_source.send "get_#{name}_info", @id »        price = @data_source.send "get_#{name}_price", @id »        result = "#{name.to_s.capitalize}: #{info} ($#{price})" »        return "* #{result}" if price >= 100 »        result  »      end       »    end         »   »    define_component :mouse »    define_component :cpu »    define_component :keyboard    end          

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. In short, you configure Heat and Ceilometer to monitor the Ceilometer metrics for a group of resources (say, VMs and you are monitoring CPU utilization). When a threshold is reached, an alarm fires, which in turn calls out to Heat to scale up (or down) the number of instances

It’s possible to run any AI algorithm on a general-purpose CPU, but the scale of computation required for AI makes using CPUs prohibitively expensive. The cost of training a single AI model—the chips it uses and the electricity they consume—can stretch into the millions of dollars. (To train a computer to recognize a cat, you have to show it a lot of cats and dogs so it learns to distinguish between the two. The more animals your algorithm requires, the more transistors you need.)

Atheism is like computer game characters misunderstanding that there has to be a CPU (Universal Central Processor).

Trap number five are low value objectives. Also know as 'who cares' OKRs. OKRs must promise clear business value, otherwise there's no value in expending resources doing them. Low value objectives, LVOs, are those for which if the objective is completed with 1.0, no one will notice or care. A classic and seductive LVO example is - increase task CPU usage by 3%. This objective by itself does not help users or Goggle directly, however, the presumably related goal 'decrease the quantity of cores requires to serve peak queries by 3% with no change to quality and latency and return the resulting excessing cores to the pool', has clear economic value. That's a superior objective.

A method call overhead of 4.5 nanoseconds for increment() and 1.4 nanoseconds for consumeCPU() would seldom be noticed with a typical business-logic method. Still, it is good to be aware that there is a slightly higher method-call cost and perhaps to avoid dynamic proxies for performance-sensitive code, especially if we are calling the proxied methods in a tight loop.

Vertical scaling, referred to as “scale up”, means the process of adding more power (CPU, RAM, etc.) to your servers. Horizontal scaling, referred to as “scale-out”, allows you to scale by adding more servers into your pool of resources.

There are at least 8 required PC components for a comfortable and effective PC experience- A CPU, at least one RAM module, at least one drive, a power supply, a motherboard, a CPU cooler, a case, ( Technically, a case isn’t required. But it makes your PC experience a great deal better ) and at least one case fan. A graphics card is also a component that most PCs have. However, it isn’t needed as long as your CPU is equipped with integrated graphics.

CPU clock speeds are barely increasing, but multi-core processors are standard, and networks are getting faster. This means parallelism is only going to increase.

The symphony that the CPU sings, makes all these unique melodies as things.

Heaven is the singularity (0neness) in the center of all consciousness; the place that manifest this physical reality. God is the CPU and heaven is the RAM; we are in the hard-drives somewhere.

Anti-religious people are often proven insane by their lack of logic: We're all inside a universal quantum computer, where God is the CPU. The idea people don't understand logic implies everything can be shown to be equations, is thus illogical, and even delusional in some cases.

Generally, the higher the clock speed, the better the CPU will perform in single-threaded applications.

Another aspect of a CPU that can boost performance is something called hyperthreading, sometimes referred to as multithreading. This is when more than one thread is assigned to a specific core, which increases the efficiency of a CPU, therefore allowing it to better or more efficiently complete a task.

The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power.

The first real chess program actually predates the invention of the computer and was written by no less a luminary than Alan Turing, the British genius who cracked the Nazi Enigma code. In 1952, he processed a chess algorithm on slips of paper, playing the role of CPU himself, and this “paper machine” played a competent game. This connection went beyond Turing’s personal interest in chess. Chess had a long-standing reputation as a unique nexus of the human intellect, and building a machine that could beat the world champion would mean building a truly intelligent machine.

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The Central Building looks like a CPU and its interconnects.

In a nutshell, Columbia experienced two failed computers, one of which we restored only to have it fail again at landing. The cause of one of the failures turned out to be a sliver of solder eleven-thousandths of an inch thick that became dislodged when the thrusters were fired, shorting out the CPU board. During the postflight debriefing, I remarked about this incident, “Had we activated the backup flight software when the problem first emerged, loss of vehicle and crew would have resulted.

Sabre’s Load-Out Omnitron III Class II Personal Assault Vehicle (Sabre) Core: Class II Omnitron Mana Engine CPU: Class D Xylik Core CPU Armor Rating: Tier IV (Modified with Adaptive Resistance) Hard Points: 5 (5 Used) Soft Points: 3 (3 Used) Requires: Neural Link for Advanced Configuration Battery Capacity: 120/120 Attribute Bonuses: +35 Strength, +18 Agility, +10 Perception Inlin Type II Projectile Rifle Base Damage: N/A (Dependent Upon Ammunition) Ammo Capacity: 45/45 Available Ammunition: 250 Standard, 150 Armor Piercing, 200 High Explosive, 25 Luminescent Ares Type II Shield Generator Base Shielding: 2,000 HP Regeneration Rate: 50/second unlinked, 200/second linked Mkylin Type IV Mini-Missile Launchers Base Damage: N/A (dependent on missiles purchased)

Tuesday liked Dorry’s dad. A lot. She wasn’t sure what he did, but he did it in a lab or a biotech company near MIT. He wasn’t very tall—Tuesday dwarfed him, and Dorry would be taller than him too, soon—and he was nerdy and bald, but he embraced his nature with dark-framed glasses and crisp collared shirts, pressed khakis and soft brown shoes. He had a nicely shaped head and dark eyes, and if he had a bit of a gut, he didn’t carry it with shame. He talked fast. Sometimes she saw a glimpse of Dorry in him, like when he looked away when he was describing something complicated, as if he were working it out as he spoke, and to make eye contact while he did so would overload his CPU. From stories Dorry had told about him, both from before and after Dorry’s mom died, Tuesday had a fuller picture of the man: He was sad. He was brilliant. Talking—especially about how he felt, but words in general—wasn’t his strongest suit. But he was trying to do what he thought was right for his daughter. Even Dorry could admit that, even if they didn’t have the same idea about what that meant.

The 8086 CPU was the first x86 processor. It was developed and manufactured by Intel, which later developed more advanced processors in the same family: the 80186, 80286, 80386, and 80486. If you remember people talking about 386 and 486 processors in the '80s and '90s, this is what they were referring to.

Turing proved, mathematically, that if you choose the right set of rules for the CPU and give it an indefinitely long tape to work with, it can perform any definable set of operations in the universe. It would be one of many equivalent machines now called Universal Turing Machines.

A CPU is just a collection of logic gates.

As with memory and CPU, there are two implementations of virtual I/O under Xen: In the paravirtualized version, there is a set of interfaces provided by the virtual-machine monitor for doing I/O, and drivers in the guest OS must be modified to use those interfaces. In the commercial version,

God may not have a brain made of neurones, or a CPU made of silicon, but if he has the powers attributed to him he must have something far more elaborately and non-randomly constructed than the largest brain or the largest computer we know.

Microprocessors commonly determine the temperature of its core through a sensor. When the core reaches its maximum junction temperature, a cooling mechanism is caused. Likewise, if the temperature level surpasses the optimum joint temperature, an alarm will be set off that warns the computer system operator to stop the process that is triggering the getting too hot of the CPU's core.

She learns that her central intelligence unit, or CIU, is part CPU and part neural network component. It lets her take in feedback and learn, much like a human brain, but in a structured way that stores and sorts memories readily in webs, so they’re easily accessible and resistant to fading.

A PC Bottleneck Calculator is a diagnostic tool that helps identify potential performance limitations in your computer by analyzing the relationship between key hardware components, primarily the CPU (Central Processing Unit) and GPU (Graphics Processing Unit). When these components aren’t properly balanced in terms of power and capability, one may constrain the performance of the other, creating a bottleneck in the system. For example, if your CPU is much more powerful than your GPU, the CPU will perform at its peak capacity, but the GPU may not be able to keep up, limiting your overall system performance, especially in tasks like gaming or rendering. Similarly, a weak CPU paired with a high-end GPU can lead to underutilization of the GPU. The PC Bottleneck Calculator evaluates your system's hardware specifications, including the CPU, GPU, RAM, and other components, and provides insights on where imbalances may exist. By using this tool, users can make informed decisions about hardware upgrades to achieve better overall performance and efficiency. It is particularly useful for gamers, content creators, and anyone who needs to optimize their computer for specific tasks or workloads.

Ernst felt Jensen was growing frustrated and would soon leave the table, so he decided to ask him something different. “Jensen, I have a two-year-old daughter at home. I bought a new Sony A100 DSLR camera and regularly download photos to my Mac to do some light editing in Photoshop. But whenever I do this, my Mac slows down as soon as I open one of these high-resolution images. It’s even worse on my Think-Pad. Can a GPU solve this problem?” Jensen’s eyes lit up. “Don’t write about this because it’s not out yet, but Adobe is a partner of ours. Adobe Photoshop with CUDA can instruct the CPU to off-load the task to the GPU, and make it much faster,” he said. “That’s exactly what I’m talking about with the coming ‘Era of the GPU.