Description

Lab Assignment L4: Code Optimization
Sanjit Seshia (sanjit+213@cs.cmu.edu) is the lead person for this assignment.
1 Introduction
This assignment deals with optimizing memory intensive code. Image processing offers many examples of functions that can benefit from optimization. In this lab, we will consider two image processing operations: rotate, which rotates an image counter-clockwise by 90◦, and smooth, which “smooths” or “blurs” an image.
For this lab, we will consider an image to be represented as a two-dimensional matrix M, where Mi,j denotes the value of (i,j)th pixel of M. Pixel values are triples of red, green, and blue (RGB) values. We will only consider square images. Let N denote the number of rows (or columns) of an image. Rows and columns are numbered, in C-style, from 0 to N − 1.
Given this representation, the rotate operation can be implemented quite simply as the combination of the following two matrix operations:
• Transpose: For each (i,j) pair, Mi,j and Mj,i are interchanged.
• Exchangerows: Row i is exchanged with row N − 1 − i.
This combination is illustrated in Figure 1.
The smooth operation is implemented by replacing every pixel value with the average of all the pixels around it (in a maximum of 3 × 3 window centered at that pixel). Consider Figure 2. The values of pixels M2[1][1] and M2[N-1][N-1] are given below:
M2
M2
j i

Figure 1: Rotation of an image by 90◦ counterclockwise

Figure 2: Smoothing an image
2 Logistics
3 Hand Out Instructions
Start by copying perflab-handout.tar to a protected directory in which you plan to do your work. Then give the command: tar xvf perflab-handout.tar. This will cause a number of files to be unpacked into the directory. The only file you will be modifying and handing in is kernels.c. The driver.c program is a driver program that allows you to evaluate the performance of your solutions. Use the command make driver to generate the driver code and run it with the command ./driver.
Looking at the file kernels.cyou’ll notice a C structure team into which you should insert the requested identifying information about the one or two individuals comprising your programming team. Do this right away so you don’t forget.
4 Implementation Overview
Data Structures
The core data structure deals with image representation. A pixel is a struct as shown below:
typedef struct { unsigned short red; /* R value */ unsigned short green; /* G value */ unsigned short blue; /* B value */
} pixel;
As can be seen, RGB values have 16-bit representations (“16-bit color”). An image I is represented as a onedimensional array of pixels, where the (i,j)th pixel is I[RIDX(i,j,n)]. Here n is the dimension of the image matrix, and RIDX is a macro defined as follows:
#define RIDX(i,j,n) ((i)*(n)+(j))
See the file defs.h for this code.
Rotate
The following C function computes the result of rotating the source image src by 90◦ and stores the result in destination image dst. dim is the dimension of the image.
void naive_rotate(int dim, pixel *src, pixel *dst) {
int i, j;
for(i=0; i < dim; i++)
for(j=0; j < dim; j++)
dst[RIDX(dim-1-j,i,dim)] = src[RIDX(i,j,dim)];
return;
}
The above code scans the rows of the source image matrix, copying to the columns of the destination image matrix. Your task is to rewrite this code to make it run as fast as possible using techniques like code motion, loop unrolling and blocking.
See the file kernels.c for this code.
Smooth
The smoothing function takes as input a source image src and returns the smoothed result in the destination image dst. Here is part of an implementation:
void naive_smooth(int dim, pixel *src, pixel *dst) {
int i, j;
for(i=0; i < dim; i++)
for(j=0; j < dim; j++)
dst[RIDX(i,j,dim)] = avg(dim, i, j, src); /* Smooth the (i,j)th pixel */
return;
}
The function avg returns the average of all the pixels around the (i,j)th pixel. Your task is to optimize smooth (and avg) to run as fast as possible. (Note: The function avg is a local function and you can get rid of it altogether to implement smooth in some other way.)
This code (and an implementation of avg) is in the file kernels.c.
Performance measures
Our main performance measure is CPE or Cycles per Element. If a function takes C cycles to run for an image of size N × N, the CPE value is C/N2. Table 1 summarizes the performance of the naive implementations shown above and compares it against an optimized implementation. Performance is shown for for 5 different values of N. All measurements were made on the Pentium III Xeon Fish machines.
The ratios (speedups) of the optimized implementation over the naive one will constitute a score of your implementation. To summarize the overall effect over different values of N, we will compute the geometric mean of the results for these 5 values. That is, if the measured speedups for N = {32,64,128,256,512} are R32, R64, R128, R256, and
R512 then we compute the overall performance as

R = p5 R32× R64× R128× R256× R512
Test case 1 2 3 4 5
Method N 64 128 256 512 1024 Geom. Mean
Naive rotate (CPE) 14.7 40.1 46.4 65.9 94.5
Optimized rotate (CPE) 8.0 8.6 14.8 22.1 25.3
Speedup (naive/opt) 1.8 4.7 3.1 3.0 3.7 3.1
Method N 32 64 128 256 512 Geom. Mean
Naive smooth (CPE) 695 698 702 717 722
Optimized smooth (CPE) 41.5 41.6 41.2 53.5 56.4
Speedup (naive/opt) 16.8 16.8 17.0 13.4 12.8 15.2
Table 1: CPEs and Ratios for Optimized vs. Naive Implementations
Assumptions
To make life easier, you can assume that N is a multiple of 32. Your code must run correctly for all such values of N, but we will measure its performance only for the 5 values shown in Table 1.
5 Infrastructure
We have provided support code to help you test the correctness of your implementations and measure their performance. This section describes how to use this infrastructure. The exact details of each part of the assignment is described in the following section.
Note: The only source file you will be modifying is kernels.c.
Versioning
You will be writing many versions of the rotate and smooth routines. To help you compare the performance of all the different versions you’ve written, we provide a way of “registering” functions.
For example, the file kernels.c that we have provided you contains the following function:
void register_rotate_functions() { add_rotate_function(&rotate, rotate_descr);
}
This function contains one or more calls to add rotatefunction. In the above example,
addrotatefunction registers the function rotate along with a string rotatedescr which is an ASCII description of what the function does. See the file kernels.c to see how to create the string descriptions. This string can be at most 256 characters long.
A similar function for your smooth kernels is provided in the file kernels.c.
Driver
The source code you will write will be linked with object code that we supply into a driver binary. To create this binary, you will need to execute the command
unix> make driver
You will need to re-make driver each time you change the code in kernels.c. To test your implementations, you can then run the command: unix> ./driver
The driver can be run in four different modes:
• Default mode, in which all versions of your implementation are run.
• Autograder mode, in which only the rotate() and smooth() functions are run. This is the mode we will run in when we use the driver to grade your handin.
• File mode, in which only versions that are mentioned in an input file are run.
• Dump mode, in which a one-line description of each version is dumped to a text file. You can then edit this text file to keep only those versions that you’d like to test using the file mode. You can specify whether to quit after dumping the file or if your implementations are to be run.
If run without any arguments, driver will run all of your versions (default mode). Other modes and options can be specified by command-line arguments to driver, as listed below:
-g : Run only rotate() and smooth() functions (autograder mode).
-f <funcfile> : Execute only those versions specified in <funcfile> (file mode).
-d <dumpfile> : Dump the names of all versions to a dump file called <dumpfile>, one line to a version (dump mode).
-q : Quit after dumping version names to a dump file. To be used in tandem with -d. For example, to quit immediately after printing the dump file, type ./driver -qd dumpfile.
-h : Print the command line usage.
Team Information
Important: Before you start, you should fill in the struct in kernels.c with information about your team (group name, team member names and email addresses). This information is just like the one for the Data Lab.
6 Assignment Details
Optimizing Rotate (50 points)
In this part, you will optimize rotate to achieve as low a CPE as possible. You should compile driver and then run it with the appropriate arguments to test your implementations.
For example, running driver with the supplied naive version (for rotate) generates the output shown below: unix> ./driver
Teamname: bovik
Member 1: Harry Q. Bovik
Email 1: bovik@nowhere.edu
Rotate: Version = naive_rotate: Naive baseline implementation:
Dim 64 128 256 512 1024 Mean
Your CPEs 14.6 40.9 46.8 63.5 90.9
Baseline CPEs 14.7 40.1 46.4 65.9 94.5
Speedup 1.0 1.0 1.0 1.0 1.0 1.0
Optimizing Smooth (50 points)
In this part, you will optimize smooth to achieve as low a CPE as possible.
For example, running driver with the supplied naive version (for smooth) generates the output shown below:
unix> ./driver
Smooth: Version = naive_smooth: Naive baseline implementation:
Dim 32 64 128 256 512 Mean
Your CPEs 695.8 698.5 703.8 720.3 722.7
Baseline CPEs 695.0 698.0 702.0 717.0 722.0
Speedup 1.0 1.0 1.0 1.0 1.0 1.0
Some advice. Look at the assembly code generated for the rotate and smooth. Focus on optimizing the inner loop (the code that gets repeatedly executed in a loop) using the optimization tricks covered in class. The smooth is more compute-intensive and less memory-sensitive than the rotate function, so the optimizations are of somewhat different flavors.
Coding Rules
• It must not interfere with the time measurement mechanism. You will also be penalized if your code prints any extraneous information.
You can only modify code in kernels.c. You are allowed to define macros, additional global variables, and other procedures in these files.
Evaluation
Your solutions for rotate and smooth will each count for 50% of your grade. The score for each will be based on the following:
• CPE: You will get full credit for your implementations of rotate and smooth if they are correct and achieve mean CPEs above thresholds Sr and Ss respectively. You will get partial credit for a correct implementation that does better than the supplied naive one.
7 Hand In Instructions
SITE-SPECIFIC: Insert a paragraph here that tells each team how to hand in their kernels.c file. For example, here are the handin instructions we use at CMU.
When you have completed the lab, you will hand in one file, kernels.c, that contains your solution. Here is how to hand in your solution:
• Make sure you have included your identifying information in the team struct in kernels.c.
• Make sure that the rotate() and smooth() functions correspondto your fastest implemnentations,as these are the only functions that will be tested when we use the driver to grade your assignement.
• Remove any extraneous print statements.
• Create a team name of the form:
– “ID” where ID is your Andrew ID, if you are working alone, or
– “ID1+ID2” where ID1 is the Andrew ID of the first team member and ID2 is the Andrew ID of the second team member.
This should be the same as the team name you entered in the structure in kernels.c.
• To handin your kernels.c file, type: make handin TEAM=teamname
where teamname is the team name described above.
• After the handin, if you discover a mistake and want to submit a revised copy, type
make handin TEAM=teamname VERSION=2
Keep incrementing the version number with each submission.
• You can verify your handin by looking in
/afs/cs.cmu.edu/academic/class/15213-f01/L1/handin
You have list and insert permissions in this directory, but no read or write permissions.
Good luck!