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|Multi core processor basics of investing||So, as the Earth system warms, most of that extra heat is actually being trapped in the ocean. People are using this knowledge to study the ocean eddy field and how it moves heat around the ocean. Multi-core processors offer significant advantages such as increased performance while using less energy, which is moving the market forward. As operators demand functionality that keeps pace with modern innovations, the size, weight and power SWaP advantages offered by MCPs are one of the few avenues through which more power can be delivered without adding more weight. In fact, there are not enough research vessels in the world to do this.|
|Multi core processor basics of investing||It gets logged onto the Internet at a global data assembly center, but it also gets sent immediately to all the operational forecasting centers in the world. China and India's growing markets for electronic components are boosting the region's market forward. The warming signal came out very quickly in the Argo data sets when averaged across the global ocean. We see the Southern Hemisphere warming faster than the Northern Hemisphere. The offers that appear in this table are from partnerships from which Investopedia receives compensation. Investing Portfolio Management.|
|Wti oil investing||When you build a long-term portfolio with core holdings, it is also easier to monitor and rebalance because it only contains a few investments. By using our websites, you agree to the placement of these cookies. During a typical day cycle, an Argo float spends most of its time at a depth of 2, meters, making readings before ascending to the surface and then transmitting its data via a satellite network. The market is being propelled forward by the growing number of Smartphone users around the world. YES - Virtual machines can be assigned individual cores to maximize processor usage NO - Virtualizing your servers allows you to maximize hardware purchases, using multi core processors gives you more resources and maximizes your investment. The market's growth is being fuelled by the rising use of Octa core processors by smart phone, laptop, tablet, and computer manufacturers such as Samsung, Qualcomm, Xiaomi, and others.|
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|Geoinvesting gbsn fda||Illustration: Frank Chimero. Developing economies like China, Japan, India, and South Korea are big contributors to growing market. Rick Hearn, senior product manager with Curtiss-Wright Defense Solutions, says MCPs allow operators to absorb numerous applications that used to run in systems across the aircraft into a single unit. And so, we want to build and sustain an array of about 1, deep-profiling floats, with an additional 1, of the newly built units capable of tracking the oceans by geochemistry. Measuring software timing behavior using single-core processors is relatively straightforward, says Wright, because you can clearly identify a deterministic worst case execution time for that software. Recommendations for Investors 2.|
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|Multi core processor basics of investing||The inevitable result of lagging behind global design trends is facing a constricting market for what has become outdated technology. And through the history of Argo, we have had issues. NO - Multi-core processors are more then yesterdays technology times two. From multicore to many-core to hard-to-describe-in-a-single-word core. Related Reports. Who should attend? Individual instructions would be parceled out to the waiting components.|
|Multi core processor basics of investing||It is ideal for pulse capture in applications such as mass spectrometry. Single User. Company Profile: Key players During a typical day cycle, an Argo float spends most of its time at a depth of 2, meters, making readings before ascending to the surface and then transmitting its data via a satellite network. Multi-core processors offer significant advantages such as increased performance while using less energy, which is moving the market forward. Wijffels: So, at the inception of the program, we put a lot of resources into a really good data-management and quality-assurance system.|
The CPU pretends it has more cores than it does, and it uses its own logic to speed up program execution. Hyper-threading allows the two logical CPU cores to share physical execution resources. This can speed things up somewhat—if one virtual CPU is stalled and waiting, the other virtual CPU can borrow its execution resources.
Your dual-core CPU with hyper-threading appears as four cores to your operating system, while your quad-core CPU with hyper-threading appears as eight cores. Hyper-threading is no substitute for additional cores, but a dual-core CPU with hyper-threading should perform better than a dual-core CPU without hyper-threading. Originally, CPUs had a single core.
That meant the physical CPU had a single central processing unit on it. A CPU with two cores, for example, could run two different processes at the same time. This speeds up your system, because your computer can do multiple things at once. Unlike hyper-threading, there are no tricks here — a dual-core CPU literally has two central processing units on the CPU chip. This helps dramatically improve performance while keeping the physical CPU unit small so it fits in a single socket.
Here, for example, you can see that this system has one actual CPU socket and four cores. Hyperthreading makes each core look like two CPUs to the operating system, so it shows 8 logical processors. Most computers only have a single CPU.
Before hyper-threading and multi-core CPUs came around, people attempted to add additional processing power to computers by adding additional CPUs. This requires a motherboard with multiple CPU sockets. Even a high-powered gaming desktop with multiple graphics cards will generally only have a single CPU. The more CPUs or cores a computer has, the more things it can do at once, helping improve performance on most tasks.
Intel CPUs also feature hyper-threading, which is kind of a bonus. We select and review products independently. When you purchase through our links we may earn a commission. Learn more. Windows ». What Is svchost. Best Cell Phone Plans. Best Camera Bags. Best Ultrawide Monitors. Best Wi-Fi 6E Routers. Best Fitness Trackers. Best SSDs for Gaming.
Best Budget Speakers. Best Mobile Hotspots. Best Speakers. Best Ergonomic Mice. Reader Favorites Best Linux Laptops. Best Wi-Fi Routers. Awesome PC Accessories. Best Wireless Earbuds. This subsection will provide an example of how to use measurements to analyze the performance of a multi-threaded application running on a multi-core CPU. The measurements can validate insights gained from other techniques and lead to new performance observations that otherwise might have been overlooked.
This particular example is a numerical solution to Laplace's equation which has multi-threaded code implemented with OpenMP. This example will investigate the performance of a numerical solution to Laplace's equation. Solutions to Laplace's equation are important in many scientific fields, such as electromagnetism, astronomy, and fluid dynamics [ Laplace's equation ]. Laplace's equation is also used to study heat conduction as it is the steady-state heat equation.
This particular code solves the steady state temperature distribution over a rectangular grid of m by n points. Examining the code of this application provides some initial insight into its expected performance behavior on a multi-core processor. Here, this particular code indicates that it is probably compute-intensive rather than memory-intensive.
It visits each point, replacing the current point average with the weighted average of its neighbors. It uses a random walk strategy and continues iterating until the solution reaches a stable state, i. At default values, this would require approximately 18, iterations.
The largest memory required is two matrices of m by n double datatypes 8 bytes , so at the default of a by matrix, the memory requirements are relatively low. Because of the high number of iterations and computations per iteration, compared with relatively low memory requirements, this application is probably more compute-intensive than memory-intensive. Further observations may be made about expected behavior using concepts of parallelism.
First, we know that this application is implemented to utilize thread-level parallelization through OpenMP. Examining the code reveals that the initialization of values, calculation loops, and most updates except calculating the current iteration difference compared to epsilon are multi-threaded. Second, thinking about the way the code executes leads to some expected data-level parallelism.
During an iteration each point utilizes portions of the same m by n matrix to calculate the average of its neighbors before writing an updated value. The impact of instruction-level parallelism will depend upon the details of the multi-core processor architecture and the type of instruction mix. This particular example was executed on an Intel Core 2 Quad Q 2.
The principal calculation in the code involves the averaging of four double-precision floating point numbers, then storing the result. Based on high-level literature, it would appear that the Intel Core architecture's Wide Dynamic Execution feature would allow each core to execute up to four of these instructions simultaneously [ Doweck06 ].
Using the code and conceptual understanding of thread-, data-, and instruction-level parallelism can lead to useful insights about expected performance. The next step is to use measurement techniques to confirm our expectations and gain further insights. Here we will focus on measuring the impact of thread-level parallelism by measuring execution time while varying the number of threads used by the application. The following results in Table 1, below, were obtained by executing the application on an Intel Q quad core four thread CPU, varying the number of threads, while profiling the application using gprof and measuring the elapsed execution time using the time command.
Table 1: gprof data and execution time for varying thread count on a quad-core CPU. From these measurements the following observations can be made. First, taking a single-thread execution as the base, we see a significant elapsed execution time improvement by moving to two threads This validates our expectation regarding thread-level parallelism; because this application employs parallelization for the majority of its routines, performance would improve significantly by assigning additional processor cores to threaded work.
This is just one example of how measurements can assist with analyzing the performance of a particular application on a multi-core CPU. If the application exists and can be tested, measurements are a robust technique to make performance observations, validate assumptions and predictions, and gain a greater understanding of the application and multi-core CPU. Performance measurements assist the analyst by quantifying the existing performance of an application.
Through profiling tools, the analyst can identify the areas of an application that significantly impact performance, quantify speedups gained by adding threads, determine if work is evenly divided, and gain other important insights. In the next section, these empirical observations will be supported with analytical models that assist with predicting performance under certain assumptions. This section introduces analytical techniques for modeling the performance of multi-core CPUs.
These techniques generate predicted performance under certain assumptions which would then validate measurements collected [ Jain91 ]. Conversely, measurements can validate the analytical models generated earlier, which is a more common sequence when a system or application does not yet exist. The three subsections introduce Amdahl's law, Gustafson's law, and computational intensity in the context of multi-core CPU performance, with examples for illustration.
This subsection will introduce Amdahl's law in the context of multi-core CPU performance and apply the law to the earlier example application that calculates Laplace's equation. Though it was conceived in , long before modern multi-core CPUs existed, Amdahl's law is still being used and extended in multi-core processor performance analysis; for example, to analyze symmetric versus asymmetric multi-core designs [ Hill08 ] or to analyze energy efficiency [ Woo08 ].
Amdahl's law describes the expected speedup of an algorithm through parallelization in relationship to the portion of the algorithm that is serial versus parallel [ Amdahl67 ]. The higher the proportion of parallel to serial execution, the greater the possible speedup as the number of processors cores increases.
This law also expresses the possible speedup by when the algorithm is improved. Subsequent authors have summarized Amdahl's textual description with the following equations in Figure 4, where f is the fraction of the program that is infinitely parallelizable, n is the number of processors cores , and S is the speedup of the parallelizable fraction f [ Hill08 ].
Figure 4: Amdahl's law - equations for speedup achieved by parallelization. These relatively simple equations have important implications for modern multi-core processor performance, because it places a theoretic bound on how much the performance of a given application may be improved by adding execution cores [ Hill08 ].
The application from section 3. Here, the first value for f , the fraction of the program that is infinitely parallelizable, was chosen to be 0. For this particular program that value is a pessimistic assumption, as the vast majority of execution time is spent in the parallelized loops, not in the single-threaded allocation or epsilon synchronization. For comparison, an optimistic value for f is also graphed at 0.
The observed values from the prior example for up to 4 cores processors are also plotted. From this illustration it is clear that the predicted speedups according to Amdahl's law are pessimistic compared to our observed values, even with an optimistic value for f. At this point it is important to discuss several significant assumptions implicit in these equations. The primary assumption is that the computation problem size stays constant when cores are added, such that the fraction of parallel to serial execution remains constant [ Hill08 ].
Other assumptions include that the work between cores is and can be evenly divided, and that there is no parallelization overhead [ Hill08 ]. Even with these assumptions, in the context of multi-core processors the analytical model provided by Amdahl's law gives useful performance insights into parallelized applications [ Hill08 ].
For example, if our goal is to accomplish a constant size problem in as little time as possible, within certain resource constraints, the area of diminishing returns in performance can be observed [ Hill08 ]. Limited analytical data is required to make a prediction, i. However, our analysis goal might instead be to evaluate whether a very large size problem could be accomplished in a reasonable amount of time through parallelization, rather than minimize execution time.
The next subsection will discuss Gustafson's law which is focused on this type of performance scenario for multi-core processors [ Hill08 ]. This subsection introduces Gustafson's law in the context of multi-core CPU performance, contrasts it with Amdahl's law, and applies the law to the earlier example for illustration and comparison. Gustafson's law also known as Gustafson-Barsis' law follows the argument that Amdahl's law did not adequately represent massively parallel architectures that operate on very large data sets, where smaller scales of parallelism would not provide solutions in tractable amounts of time [ Hill08 ].
Here, the computation problem size changes dramatically with the dramatic increase in processors cores ; it is not assumed that the computation problem size will remain constant. Instead, the ratio of parallelized work to serialized work approaches one [ Gustafson88 ]. The law is described by the equation in Figure 6 below, where s' is the serial time spent on the parallel system, p' is the parallel time spent on the parallel system, and n is the number of processors [ Gustafson88 ].
Figure 6: Gustafson's law - equation for scaled speedup. This law was proposed by E. Barsis as an alternative to Amdahl's law after observing that three different applications running on a processor hypercube experienced a speedup of about x, for sequential execution percentages of 0.
According to Amdahl's law, the speedup should have been x or less. Gustafson's law operates on the assumption that when parallelizing a large problem, the problem size is increased and the run time is held to a constant, tractable level. This proposal generated significant controversy and renewed efforts in massively parallel problem research that would have seemed inefficient according to Amdahl's law [ Hill08 ][ Gustafson88 ].
Applying Gustafson's law to our earlier example provides a prediction that by increasing problem size and processors cores , and keeping our desired time constant, we will achieve speedups roughly proportional to the number of processors. Here, in Figure 7, we used the same portion of parallel time 0.
From the graph it is evident that Gustafson's law is more optimistic than Amdahl's law about the speedups achieved through parallelization on a multi-core CPU, and that the curve is similar to the speedups observed in this particular example.
Another implication is that the amount of time spent in the serial portion becomes less and less significant as the number of processors and problem size is increased. For the analyst, Gustafson's law is a useful approximation of potential speedups through parallelization when the data set is large, the problem is highly parallelizable, and the goal is to solve the problem within a set amount of time that would otherwise be unacceptably long [ Hill08 ].
The next subsection introduces analytical techniques for computational intensity that can assist with formulating a more concrete performance bound for a given problem. This subsection will introduce analytical techniques for determining computational and memory intensity in the context of multi-core CPU performance.
This section draws heavily upon the well illustrated, step-by-step approach in [Chandrawmowlishwaran10], and the interested reader is encouraged to refer to their work for a detailed example of multi-core performance analysis regarding a multi-threaded implementation of the Fast Multipole Method. This subsection will be illustrated with a simple example derived from the Laplace's equation problem explored in prior sections.
Modeling computations in terms of compute intensity and memory intensity is a low-level technique to analyze the probable bottleneck of a given portion of an application. It is focused on answering the question whether that portion is compute-bound or memory-bound, such that performance improvements can be focused on the most limiting factor [ Chandramowlishwaran10 ]. It can also provide probable answers to whether the memory required for a given portion would fit in the CPU cache; cache misses are expensive because of the additional delay involved with accessing system Random Access Memory RAM , and identifying this problem may guide optimizations to reduce cache misses [ Chandramowlishwaran10 ].
The analysis of computational intensity often begins with examining the asymptotic bounds of the algorithm, as this provides an upper bound on the growth rate of the function as the problem size n increases to infinity. This will provide an upper bound on the number of computations per n. The next step is to inspect the source code to approximately count the number of operations for each computation.
Next, for a given operation, the number and type of instructions that would be executed by the CPU for each operation is counted. Then, taking into account possible instruction level parallelism, instruction latencies, and throughputs specific to the particular processor architecture, an estimated number of clock cycles required for each operation may be computed. The estimated number of clock cycles per operation is then multiplied by the number of operations per n and the expected number of n.
This result is the expected number of CPU clock cycles for the problem size n , which may then be divided by the clock frequency to achieve an estimate of the CPU time required. A similar method derives an estimate of the memory intensity. Here, the constants behind the asymptotic bound of space complexity, which are often omitted with big- O notation, are useful.
These constants together with the bound give an estimate of the space complexity in terms of n. Examining the code will then determine the amount of memory bytes read and written for each n. Multiplying these terms provides the expected bytes in terms of n. Plugging in the given problem size n and then dividing by the bus speed in terms of bytes per second will provide an estimate of the time spent reading and writing to memory.
The compute intensity and memory intensity may then be compared to determine which is the bounding factor. Returning to the previous example that computed Laplace's equation, based on the code we expected the primary portion of the application to be compute-intensive rather than memory-intensive. The following "back-of-the-envelope" calculations are derived in the step-by-step fashion described above. Following this example, the computational and memory intensity calculations support our earlier expectations.
Because the computational intensity is about 1. This subsection introduced analytical techniques for determining computational and memory intensity in the context of multi-core CPU performance. The step-by-step technique assists in producing estimates that indicate whether a particular operation is compute-bound or memory-bound, which will assist in further analysis or optimization. This section introduced analytical modeling techniques that assist in quantifying multi-core processor performance.
Amdahl's law makes predictions about potential speedup through parallelization given a particular ratio of serial to parallel work. Gustafson's law changes assumptions to produce a more optimistic model for highly parallelized workloads with large data sets.
Finally, the computational and memory intensity techniques assist the analyst in identifying compute-bound versus memory-bound operations. In this paper the goal was to provide an overview of multi-core processor characteristics, performance behavior, measurement tools, and analytical modeling tools to conduct performance analysis. Because understanding the behavior and architecture of a multi-core processor is necessary to proficiently analyze its performance, the first section defined a multi-core processor in general terms, then introduced the primary types of multi-core processors used in computing—CPUs, GPUs, and FPGAs.
The second section introduced three types of parallelism that impact multi-core processor performance: instruction-level parallelism, thread-level parallelism, and data-level parallelism. Each technique has potential performance benefits and detriments, and in-depth analysis of the underlying processor architecture, thread performance factors, and data coherence performance factors will lead to improved analysis.
The third section discussed profiling and benchmarking tools that assist the analyst in measuring performance of a multi-core CPU. This section also introduced an example program that solves Laplace's equation; this program's performance measurement results were then reviewed for insights. The fourth section introduced analytical modeling techniques that assist in quantifying multi-core processor performance.
This section compared Amdahl's predictions about serial versus parallel performance scaling with Gustafson's predictions and with the performance of the example application. Lastly, the fourth section introduced techniques to estimate computational and memory intensity and provided calculations using the example problem for illustration.