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Writing Efficient C and C Code Optimization. In this article, I have gathered all the experiences and information, which can be applied to make a C code optimized for speed as well as memory. Introduction. During a project for developing a light JPEG library which is enough to run on a mobile device without compromising quality graphics on a mobile device, I have seen and worked out a number of ways in which a given computer program can be made to run faster. In this article, I have gathered all the experiences and information, which can be applied to make a C code optimized for speed as well as memory. Although a number of guidelines are available for C code optimization, there is no substitute for having a thorough knowledge of the compiler and machine for which you are programming. Often, speeding up a program can also cause the code's size to increase. This increment in code size can also have an adverse effect on a program's complexity and readability. It will not be acceptable if you are programming for small device like mobiles, PDAs etc., which have strict memory restrictions. So, during optimization, our motto should be to write the code in such a way that memory and speed both will be optimized. Declaration. Actually, during my project, I have used the tips from this for optimization ARM because my project was on ARM platform, but I have also used many other articles from Internet. All tips of every article do not work well, so I collect only those tips together, which are very useful and very efficient. Also, I have modified some of them in such a way that they are almost applicable for all the environments apart from ARM. What I did is just make a collection of the information from various sites but mostly from that PDF file I mentioned above. I never claimed that these are my own discoveries. I have mentioned all information sources in the References section at the end of this article. Where it is needed? Without this point, no discussion can be started. First and the most important part of optimizing a computer program is to find out where to optimize, which portion or which module of the program is running slow or using huge memory. If each part is separately being optimized then the total program will be automatically faster. The optimizations should be done on those parts of the program that are run the most, especially those methods which are called repeatedly by various inner loops that the program can have. For an experienced programmer, it will usually be quite easy to find out the portions where a program requires the most optimization attention. But there are a lot of tools also available for detecting those parts of a program. I have used Visual C ++ IDE's in- built profiler to find out where the program spends most click tricks. Another tool I have used is Intel Vtune, which is a very good profiler for detecting the slowest parts of a program. In my experience, it will usually be a particular inner or nested loop, or a call to some third party library methods, which is the main culprit for running the program slow. Integers. We should use unsignedint instead of int if we know the value will never be negative. Some processors can handle unsigned integer arithmetic considerably faster than signed (this is also good practice, and helps make for self- documenting code). So, the best declaration for an int variable in a tight loop would be: register unsigned int variable. But it may not be applicable for all compilers. Remember, integer arithmetic is much faster than floating- point arithmetic, as it can usually be done directly by the processor, rather than relying on external FPUs or floating point math libraries. We need to be accurate to two decimal places (e. The division function takes a constant time plus a time for each bit to divide. Time (numerator / denominator) = C0 + C1* log. C0 + C1 * (log. 2 (numerator) - log. The current version takes about 2. N cycles for an ARM processor. As an expensive operation, it is desirable to avoid it where possible. Sometimes, such expressions can be rewritten by replacing the division by a multiplication. For example, (a / b) > c can be rewritten as a > (c * b) if it is known that b is positive and b *c fits in an integer. It will be better to use unsigned division by ensuring that one of the operands is unsigned, as this is faster than signed division. Combining division and remainder. Both dividend (x / y) and remainder (x % y) are needed in some cases. This site is all about hacking things into what you want. You can find project tutorials about the latest technology trends ranging from robotics, opencv.
So, such traits are used to tell OpenCV which data type you are working with, even if such a type is not native to OpenCV. For example, the matrix B initialization above is compiled because OpenCV defines the proper. Manish Bhojasia, a technology veteran with 19+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer and SAN Architect and is passionate about competency developments in these areas. In such cases, the compiler can combine both by calling the division function once because as it always returns both dividend and remainder. If both are needed, we can write them together like this example: int func. The compiler uses a shift to perform the division. Therefore, we should always arrange, where possible, for scaling factors to be powers of two (for example, 6. And if it is unsigned, then it will be more faster than the signed division. The signed division will take more time to execute because it rounds towards zero, while a shift rounds towards minus infinity. An alternative for modulo arithmetic. We use remainder operator to provide modulo arithmetic. But it is sometimes possible to rewrite the code using if statement checks. Consider the following two examples: uint modulo. Note that the new version only works if it is known that the range of count on input is 0- 5. Using array indices. If you wished to set a variable to a particular character, depending upon the value of something, you might do this: switch ( queue ) . Global variables can be changed by assigning them indirectly using a pointer, or by a function call. Hence, the compiler cannot cache the value of a global variable in a register, resulting in extra (often unnecessary) loads and stores when globals are used. We should therefore not use global variables inside critical loops. If a function uses global variables heavily, it is beneficial to copy those global variables into local variables so that they can be assigned to registers. This is possible only if those global variables are not used by any of the functions which are called. For example: int f(void). If we know it won't be altered, we could code it like this instead: void func. The live- range of a variable is defined as all statements between the last assignment to the variable, and the last usage of the variable before the next assignment. In this range, the value of the variable is valid, thus it is alive. In between live ranges, the value of a variable is not needed: it is dead, so its register can be used for other variables, allowing the compiler to allocate more variables to registers. The number of registers needed for register- allocatable variables is at least the number of overlapping live- ranges at each point in a function. If this exceeds the number of registers available, some variables must be stored to memory temporarily. This process is called spilling. The compiler spills the least frequently used variables first, so as to minimize the cost of spilling. Spilling of variables can be avoided by: Limiting the maximum number of live variables: this is typically achieved by keeping expressions simple and small, and not using too many variables in a function. Subdividing large functions into smaller, simpler ones might also help. However, such a variable may still be spilled in some circumstances. Variable Types. The C compilers support the basic types char, short, int and long (signed and unsigned), float and double. Using the most appropriate type for variables is very important, as it can reduce code and data size and increase performance considerably. Local variables. Where possible, it is best to avoid using char and short as local variables. For the types char and short, the compiler needs to reduce the size of the local variable to 8 or 1. This is called sign- extending for signed variables and zero extending for unsigned variables. It is implemented by shifting the register left by 2. These shifts can be avoided by using int and unsignedint for local variables. This is particularly important for calculations which first load data into local variables and then process the data inside the local variables. Even if data is input and output as 8- or 1. Consider the following three example functions: int wordinc (int a). I've seen programs that pass structures several Kilo Bytes in size by value, when a simple pointer will do the same thing. Functions receiving pointers to structures as arguments should declare them as pointer to constant if the function is not going to alter the contents of the structure. As an example: void print. It also ensures that the compiler will trap any accidental attempts by your code to write to the read- only structure and give an additional protection to the content of the structure. Pointer chains. Pointer chains are frequently used to access information in structures. For example, a common code sequence is: typedef struct . A better version would cache p- > pos in a local variable: void Init. Pos. 2(Object *p). Conditional execution is disabled for code sequences which contain function calls, as on function return the flags are destroyed. It is therefore beneficial to keep the bodies of if and else statements as simple as possible, so that they can be conditionalized. Relational expressions should be grouped into blocks of similar conditions. The following example shows how the compiler uses conditional execution: int g(int a, int b, int c, int d). This is especially beneficial if min is zero. The same example after this optimization: bool Point. In. Rectangel. Area (Point p, Rectangle *r). The flags can also be set by other operations, such as MOV, ADD, AND, MUL, which are the basic arithmetic and logical instructions (the data processing instructions). If a data processing instruction sets the flags, the N and Z flags are set the same way as if the result was compared with zero. The N flag indicates whether the result is negative, the Z flag indicates that the result is zero. Dropping the heavy data structures of tradition geometry. The tutorial is a self- contained. Via interactive, step- by- step examples, we. Each section of the. Table of contents. Chapter 1. We introduce libigl with a series of self- contained examples. The purpose of. each example is to showcase a feature of libigl while applying to a practical. In this chapter, we will present the basic. All the tutorial examples are. Mac. OSX, Linux and Windows. Before getting into the examples, we summarize the main design principles in. No complex data types. We mostly use matrices and vectors. This greatly. favors code reusability and forces the function authors to expose all the. We use external libraries only when necessary and. Header- only. It is straight forward to use our library since it is only. Every function (including its full. Downloading libigllibigl can be downloaded from our github. The core libigl functionality only depends on the C++ Standard Library and. Eigen. To build all the examples in the tutorial, you can use the CMake. Lists. txt in. the tutorial folder: cd tutorial. DCMAKE. On Ubuntu, you need to install the following packages: sudo apt- get install git. A few examples in Chapter 5 requires the Co. Mi. So. solver. We provide a. Co. MISo that works out of the box with libigl. To install it: cd libigl/external. Co. MISo. git. You can then build the tutorials again and it libigl will automatically find and. Co. MISo. Note 1: Co. MISo is distributed under the GPL3 license, it does impose restrictions on commercial usage. Note 2: Co. MISo requires a blas implementation. We use the built- in blas in macosx and linux, and we bundle a precompiled binary for VS2. Do NOT compile the tutorials. Eigen library to encode vector. We suggest that you keep the. A triangular mesh is encoded as a pair of matrices: Eigen: :Matrix. Xd V. Eigen: :Matrix. Xi F. V is a #N by 3 matrix which stores the coordinates of the vertices. Each. row stores the coordinate of a vertex, with its x,y and z coordinates in the first. The matrix F stores the triangle. F denotes a triangle whose 3 vertices are. V. A simple mesh made of 2 triangles and 4 vertices. Note that the order of the vertex indices in F determines the orientation of. As a general. rule each libigl function is contained in a pair of . The viewer supports the following callbacks: bool (*callback. This has to be called. Every. callback returns a boolean value that tells the viewer if the event has been. This is. useful, for example, to disable the default mouse event handling if you want to. The viewer can be extended using plugins, which are classes that implements all. C must have as many. The. scalar function is converted to colors using a color transfer function, which. A simple example of a scalar. V. (Example 1. 04). The function igl: :jet can be used to. Eigen: :Vector. Xd Z = V. Z,true,C). The first row extracts the third column from V (the z coordinate of each. The second parameter of jet normalizes the scalar field to lie between 0 and 1 before applying the transfer function.(Example 1. Not committing to. In addition to plotting the surface, the viewer supports the visualization of points, lines and text labels: these overlays can be very helpful while developing geometric processing algorithms to plot debug informations. The point is placed at the coordinates specified in each row of P, which is a #P by 3 matrix. Both P1 and P2 are of size #P by 3. For example. the bounding box of a mesh can be found by taking the colwise maximum and minimum of V: Eigen: :Vector. V. colwise(). min. Coeff(). Eigen: :Vector. M = V. colwise(). Coeff(). (Example 1. The bounding box of a mesh is shown. As of version 1. 2 the viewer uses a new menu and completely replaces. Ant. Tweak. Bar. It is based on the. To extend the default menu of the. Viewer viewer. bool bool. Variable = true. float float. Variable = 0. 1f. Orientation . It also provides an introduction to basic drawing and coloring routines of our viewer. Normals. Surface normals are a basic quantity necessary for rendering a surface. There. are a variety of ways to compute and store normals on a triangle mesh. Example 2. 01 demonstrates how to compute and visualize normals with libigl. Per- face. Normals are well defined on each triangle of a mesh as the vector orthogonal to. These piecewise- constant normals produce piecewise- flat. Per- vertex. Normals can be computed and stored on vertices, and interpolated in the interior of the triangles to produce smooth renderings (Phong shading). The main difference between these techniques is their weighting scheme: Uniform. The typical half- edge style computation of area- based weights has this structure: N. Zero(V. rows(),3). However, per- vertex normals may be throwing each face normal to. N. set. Zero(V. rows(),3). F. rows(); f++). . This format is common to. Open. GL and the . Often such normals are tuned by the mesh. Libigl. implements a simple scheme which computes corner normals as averages of. The directions of maximum and. Mean curvature is defined as the average of principal curvatures: \(H = \frac. The result is a so- called mean- curvature. Delta \mathbf. In the neighborhood around every vertex, a. Example. 2. 03). The Curvature. Directions example computes principal curvatures via quadric. Gradient. Scalar functions on a surface can be discretized as a piecewise linear function. Thus our discrete gradient operator can be written. This matrix \(\mathbf. Many. interpretations and flavors of the Laplace and Laplace- Beltrami operator exist. In open Euclidean space, the Laplace operator is the usual divergence of. Laplacian of a function is the trace of its. Hessian): \(\Delta f =. The most popular in geometry. Laplacian” \(\mathbf. As a linear operator taking vertex values to vertex values, the. Laplacian \(\mathbf. However, this is not the case, since the Laplacian may be built by summing together contributions for each triangle, much in spirit with its FEM discretization. Dirichlet energy (sum of squared gradients): for(triangle t : triangles). Note that this is equivalent to minimizing surface area. The Laplacian example computes conformalized mean curvature flow using the. Laplacian . From an FEM point of view, it is a discretization of. Consequently. \(\mathbf. The inverse of. this matrix is also very useful as it transforms integrated quantities into. Delta f \approx \mathbf. This matrix represents an. This may be derived by applying. Green’s identity (ignoring boundary conditions for the moment): \(\int. This also. hints that we may consider \(\mathbf. Besides geometry processing routines, libigl has linear algebra. Eigen and make it feel even more similar to a high- level. Matlab. Slice. A very familiar and powerful routine in Matlab is array slicing. This allows. reading from or writing to a possibly non- contiguous sub- matrix. Let’s consider. the Matlab code: B = A(R,C). If A is a \(m \times n\) matrix and R is a \(j\)- long list of row- indices. C is a \(k\)- long list of column- indices, then as a. B will be a \(j \times k\) matrix drawing elements from A according to. R and C. In libigl, the same functionality is provided by the slice. Example 3. 01): Vector. Xi R,C. functionality is provided in libigl using slice. For example in Matlab, one can write. The second. output I is a \(m \times n\) matrix of indices such that Y(i,j) =. X(I(i,j),j). That is, I reveals how X is sorted into Y. This same functionality is supported in libigl: igl: :sort(X,1,true,Y,I). Similarly, sorting entire rows can be accomplished in Matlab using. Matlab’s hsv. 2rgb)igl: :intersect. Set intersection of matrix elements. Determine whether matrix is diagonaligl: :jet. Quantized colors along the rainbow. Compute the median per columnigl: :mode. Compute the mode per columnigl: :null. Compute the null space basis of a matrixigl: :nchoosek. Compute all k- size combinations of n- long vectorigl: :orth. Orthogonalization of a basisigl: :parula. Generate a quantized colormap from blue to yellowigl: :randperm. Generate a random permutation of . Matlab’s rgb. 2hsv)igl: :setdiff. Set difference of matrix elementsigl: :sort. Sort elements or rows of matrixigl: :speye. Identity as sparse matrixigl: :sum. Sum along columns or rows (of sparse matrix)igl: :unique. Extract unique elements or rows of matrix. Laplace equation. A common linear system in geometry processing is the Laplace equation: \(. Their values are known so we may move their corresponding terms to. Conceptually, this is very easy if we have sorted \(\mathbf. One option would be to sort V. Z to match V. However. With array slicing no explicit sort is needed. Instead we can slice- out. Then we can slice the solution into the. Z corresponding to the interior vertices (Example 3. The Laplace. Equation example solves a Laplace equation with Dirichlet. Quadratic energy minimization. The same Laplace equation may be equivalently derived by minimizing Dirichlet. Precomputation only depends on the. Let’s consider another example, this time. Specifically let’s solve the. Laplace equation or equivalently minimize the Laplace energy: \. The right- hand. side Beq is simply zero. Internally, min. This method adds additional variables for each linear constraint (in. The only. difference from the straight quadratic minimization system, is that this. Thus, we must use a. LDLT rather than LLT): libigl’s. Specifically box constraints (lower and. This algorithm works by iteratively “activating” violated inequality. After deciding which constraints are active at each iteration, the problem. This is repeated until convergence. Currently the implementation is efficient for box constraints and sparse. Unlike alternative interior- point methods, the active set method benefits from. Most commonly in geometry processing, we let \(A=L\) the cotangent. Laplacian and \(B=M\) the per- vertex mass matrix (e. For a discrete triangle mesh, a. Libigl implements a generalized eigen problem solver so. Often the sum above is truncated to the first \(k\) eigen vectors. If the low. frequency modes are chosen, i. 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University locations in Fairfield, Connecticut and near Boston, Chicago, Philadelphia and Atlanta bring together . Learn creative and technical skills > Join a development team > Build a playable game > Present to a panel of experts > Tour a game design studio > Learn from guest speakers. Teens 1. 5 . Will you be a 3. D Animator, Level Designer, Audio Artist, or Programmer? Present your game to a panel of industry experts, tour a game studio and learn from game studio guest speakers. Studios providing tours and speakers have included Electronic Arts, Blizzard Entertainment, Rock Star Games, Turbine Inc, Hi- Rez Studios. Offered at Bentley University near Boston and Mercer University in Atlanta, Emagination Game Design attracts teens from all across America and many foreign countries. We help with transportation by picking up and dropping off at airport and train terminals. Participating students have the opportunity learn about the field of psychology, explore the major of psychology, and get a taste of college life. Students have a chance to meet peers, learn from faculty, and gain hands- on experiences in the area of psychology. Participants: Rising high school sophomores, juniors, and seniors. Program website: Gettysburg College Summer Psychology Camp. Program: i. D Tech Camps ! Students ages 7- 1. Minecraft, engineer robots, model 3. D characters, build websites, print 3. D models, and more! These weeklong, day and overnight summer programs are held at prestigious campuses nationwide, including Stanford, Caltech, Rice, Northwestern, Emory, Princeton, Columbia, Yale, and more. D Tech summer courses are inclusive of every skill level and learning style. We guarantee an 8: 1 student- to- instructor ratio and every location so both beginners and advanced students will receive personalized, 1- on- 1 instruction. D Tech instructors are recruited from top universities like UCLA, Stanford, and NYU, and they. Your child will meet new friends, use big- name software from companies like Autodesk and Adobe, learn awesome STEM skills, and build a foundation of knowledge that will make a positive impact in school, college, and future careers. D Tech also offers early- education STEM programs, all- girls tech camps, teen- only academies, and an online learning platform. Visit us online at our website to find what! They meet new friends with similar interests, see what it. Our elite academy faculty are recruited from top universities like UCLA, Stanford, and NYU, and they. In addition, i. D Tech also offers weeklong summer programs for ages 7- 1. STEM programs for ages 6- 9, all- girls tech camps, and an online learning platform. More than 2. 35,0. D Tech summer program since 1. Facebook and Google, publish award- winning apps, start nonprofits and businesses, and more! Visit us online at our website to find what. Summer Program Website: i. D Game Design & Development Academy Summer Pre- College Program for Teens. Program: Julian Krinsky Summer Internships in Philadelphia for High School Students: Philadelphia, Pennsylvania. Description: There is no better way to understand a job than by actually doing it. Get one of the best summer internships in Philadelphia before you head off to college! You might be interning with a lawyer, developing marketing campaigns, shadowing a doctor, assisting a vet, or involved with high- profile mentors. Live on campus at the University of Pennsylvania. College credit is now available. Participants: High school students, ages 1. Program website: Julian Krinsky Summer Internships. Program: Julian Krinsky Xploration Summer Programs for Middle School Students: Philadelphia, Pennsylvania. Description: Julian Krinsky’s middle school summer camp is for students ages 1. Students can choose two classes each week. They can follow their passions or try something new in the areas that interest them most. Students come from over 3. Haverford College near Philadelphia. There are also evening activities and weekend trips to places such as NYC, Washington DC, amusement parks, and more! Participants: Middle school students, ages 1. Program website: Julian Krinsky Xploration Summer Enrichment Program. Program: University of Pennsylvania Summer Architecture Program for High School Students: Philadelphia, Pennsylvania. Description: In- depth architecture program for students ages 1. University of Pennsylvania. Working alongside a culturally diverse mix of students, you’ll use the city of Philadelphia’s architecture as inspiration and produce your own portfolio. Participants: High school students, ages 1. Program website: University of Pennsylvania Summer Architecture. Program: University of Pennsylvania Summer Art Program for High School Students: Philadelphia, Pennsylvania. Description: This pre- college art program prepares students ages 1. You’ll work with Penn Faculty in Penn’s art studios as you create art and develop your artistic expression. Participants: High school students, ages 1. Program website: University of Pennsylvania Summer Pre- College Art Program. Program: University of Pennsylvania – The Engineering Summer Academy for High School Students: Philadelphia, Pennsylvania. Description: The School of Engineering at the University of Pennsylvania offers a unique summer opportunity for a selective group of motivated and talented high school teens. High school students work with leading faculty, live on Penn. ESAP is, in part, designed to provide exceptional math and science students the opportunity to explore the field of Engineering before applying to college. Situated on our historic Philadelphia campus, right in the heart of the city, pre- college students can also take advantage of the vast array of activities and resources available to the Penn community. Coursework incorporates exciting STEM (Science, Technology, Engineering, and Math) subjects and material. High school students enroll in one of six courses offered. Courses include: Biotechnology, Computer Graphics, Computer Science, Engineering Complex Networks, Nanotechnology, Robotics. Program Dates: For more information, please visit: www. Participants: High school students rising 1. Program website: The Engineering Summer Academy at Penn. Program: University of Pennsylvania International Affairs Summer Program with Model UN for High School Students: Philadelphia, Pennsylvania. Description: Learn leadership through Model UN! If you’re just getting interested in world relations or are already a student of global politics, our pre- college program will increase your experience, knowledge and skills for future leadership. |
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