Rel r latina-rel function | R Documentation

This example shows how to use pagefun to improve the performance of applying a large number of independent rotations and translations to objects in a 3-D environment. This is typical of a range of problems which involve a large batch of calculations on small arrays. GPUs are most effective when carrying out calculations on very large matrices. When you have a large data set but your calculations are divided into many small matrix operations, it can be challenging to maximize performance by running simultaneously on the many hundreds of GPU cores. The arrayfun and bsxfun functions allow scalar operations to be carried out in parallel on the GPU.

Rel r latina

Rel r latina

Rel r latina

Rel r latina

The pagefun function adds the capability of carrying out matrix operations in batch in a similar way. It makes sure all GPU operations have finished before recording the time. The complaint also alleged that the defendants made false representations that: 1 a public offering of LinkNet stock was imminent; LinkNet's stock would shortly be listed Rel r latina NASDAQ; investors could realize phenomenal returns on their investment in a short Airbrush tanning industry and LinkNet and Latina had contracts for the sale of long distance service in the United States and Mexico which would Rel r latina millions of dollars in revenue to the companies. This is typical of a range of problems which involve a large batch Voyers on video calculations on small arrays. The GPU version above was very slow because, although all calculations were independent, they ran in series. Now we have got multiple robots as well as multiple objects. In addition, the Order bars Isaac from participating in any offering of penny stock. Trials Product Updates. Set up the map Let's create a map of objects with randomized positions and orientations in a large room.

Nipple clamp reviews. Litigation Release No. 18792 / July 26, 2004

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This example shows how to use pagefun to improve the performance of applying a large number of independent rotations and translations to objects in a 3-D environment. This is typical of a range of problems which involve a large batch of calculations on small arrays. GPUs are most effective when carrying out calculations on very large matrices. When you have a large data set but your calculations are divided into many small matrix operations, it can be challenging to maximize performance by running simultaneously on the many hundreds of GPU cores.

The arrayfun and bsxfun functions allow scalar operations to be carried out in parallel on the GPU. The pagefun function adds the capability of carrying out matrix operations in batch in a similar way. In this example, a robot is navigating a known map containing a large number of features that the robot can identify using its sensors. The robot locates itself in the map by measuring the relative position and orientation of those objects and comparing them to the map locations.

Assuming the robot is not completely lost, it can use any difference between the two to correct its position, for instance by using a Kalman Filter. We will focus on the first part of the algorithm. We represent positions and orientations using 3-by-1 vectors T and 3-by-3 rotation matrices R. When we have N of these transforms we pack the translations into a 3-by-N matrix, and the rotations into a 3-byby-N array.

The following function initializes N transforms with random values, providing a structure as output:. To correctly identify map features the robot needs to transform the map to put its sensors at the origin. Then it can find map objects by comparing what it sees with what it expects to see.

For a map object we can find its position relative to the robot and orientation by transforming its global map location:. We need to transform every map object to its location relative to the robot. We can do this serially by looping over all the transforms in turn. Note the 'like' syntax for zeros which will allow us to use the same code on the GPU in the next section.

To time the calculation we use the timeit function, which will call loopingTransform multiple times to get an average timing. Since it requires a function with no arguments, we use the syntax to create an anonymous function of the right form.

To run this code on the GPU is merely a matter of copying the data into a gpuArray. Now we call gputimeit , which is the equivalent of timeit for code that includes GPU computation.

It makes sure all GPU operations have finished before recording the time. The GPU version above was very slow because, although all calculations were independent, they ran in series. Using pagefun we can run all the computations in parallel. We also employ bsxfun to calculate the translations, since these are element-wise operations.

The first computation was the calculation of the rotations. We pass this to pagefun along with the two sets of rotations to be multiplied:. R' is a 3-by-3 matrix, and Map. R is a 3-byby-N array. The pagefun function matches each independent matrix from the map to the same robot rotation, and gives us the required 3-byby-N output. The translation calculation also involves a matrix multiply, but the normal rules of matrix multiplication allow this to come outside the loop without any changes.

However, it also involves subtracting Robot. T from Map. T , which are different sizes. Since this is an element-by-element operation, we can use bsxfun to match up dimensions in the same way as pagefun did for the rotations:.

This time we needed to use the element-wise operator which maps to the function minus -. If our robot was in an unknown part of the map, it might use a global search algorithm to locate itself. The algorithm would test a number of possible locations by carrying out the above computation and looking for good correspondence between the objects seen by the robot's sensors and what it would expect to see at that position.

Now we have got multiple robots as well as multiple objects. To distinguish 'robot space' from 'object space' we use the 4th dimension for rotations and the 3rd for translations.

That means our robot rotations will be 3-bybyby-M, and the translations will be 3-byby-M. We initialize our search with random robot locations. A good search algorithm would use topological or other clues to seed the search more intelligently.

Our new looping transform function requires two nested loops, to loop over the robots as well as over the objects. For our GPU timings we use tic and toc this time, because otherwise the calculation would take too long. This will be precise enough for our purposes. To ensure any cost associated with creating the output data is included, we are calling loopingTransform2 with a single output variable, just as timeit and gputimeit do by default.

As before, the looping version runs much slower on the GPU because it is not doing calculations in parallel. The new pagefun version needs to incorporate the transpose operator as well as mtimes into a call to pagefun. We also need to squeeze the transposed robot orientations to put the spread over robots into the 3rd dimension, to match the translations. Despite this, the resulting code is considerably more compact.

Once again, pagefun and bsxfun expand dimensions appropriately. So where we multiply 3-bybyby-M matrix Rt with 3-byby-N-by-1 matrix Map. R , we get a 3-byby-N-by-M matrix out. The pagefun function supports a number of 2-D operations, as well as most of the scalar operations supported by arrayfun and bsxfun.

Together, these functions allow you to vectorize a range of computations involving matrix algebra and array manipulation, eliminating the need for loops and making huge performance gains. Wherever you are doing small calculations on GPU data in a loop, you should consider converting to a batch implementation in this way. This can also be an opportunity to make use of the GPU to improve performance where previously it gave no performance gains.

A modified version of this example exists on your system. Do you want to open this version instead? Choose a web site to get translated content where available and see local events and offers.

Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance. Other MathWorks country sites are not optimized for visits from your location. Toggle Main Navigation. All Examples Functions More. Search MathWorks. Open Mobile Search. All Examples Functions. Toggle navigation. Trials Product Updates. Open Script. Set up the map Let's create a map of objects with randomized positions and orientations in a large room.

R , 'like' , Map. T , 'like' , Map. R :,:,i ; Rel. T :,i - Robot. T ; end end. It takes 0. R ; gMap. T ; gRobot. R ; gRobot.

Unvectorized GPU code is R', Map. R ; Rel. T, Robot. Vectorized GPU code is It takes 7. T ; end. No, overwrite the modified version es Yes es. Select a Web Site Choose a web site to get translated content where available and see local events and offers.

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On July 19, the Honorable Nora M. Manella, U. Isaac enjoining him from future violations of the antifraud, securities registration and broker-dealer registration provisions of the federal securities laws. Johnson, Joseph W. Isaac and Dale R. Carone with the fraudulent offer and sale of unregistered securities of LinkNet and Latina. The complaint also alleged that the defendants made false representations that: 1 a public offering of LinkNet stock was imminent; LinkNet's stock would shortly be listed on NASDAQ; investors could realize phenomenal returns on their investment in a short time; and LinkNet and Latina had contracts for the sale of long distance service in the United States and Mexico which would generate millions of dollars in revenue to the companies.

The complaint further alleged that, while the offerings were ongoing, Isaac sold personal shares of LinkNet and Latina stock through the Encino boiler room and by other means. The order against Issac prohibits him from future violations of Sections 5 and 17 a of the Securities Act of and Sections 10 b and 15 a of the Securities Exchange Act of and Rule 10b-5 promulgated thereunder. In addition, the Order bars Isaac from participating in any offering of penny stock.

Home Previous Page. Carone, et al. Docket No.

Rel r latina

Rel r latina

Rel r latina