Due Monday, April 26th
This project is designed to be both a C project as well as a performance project. In this project you will be implementing a slower version of numpy. Your version of numpy, numc
(how exciting!), is most likely to be slower than numpy, but much faster than the naive implementations of matrix operations. You will first complete a naive solution for some matrix functions in C, then you will experiment with the setup file in Python to install your numc
module. After that, you will gain a deeper understanding of the Python-C interface by overloading some operators and defining some instance methods for numc.Matrix
objects. Finally, you will speed up your naive solution, thus making numc.Matrix
operations faster.
Do not expect your final completed numc
module to be as good as numpy, but you should expect a very large speedup compared to the naive solution, especially for matrix multiplication and exponentiation!
src/numc.c
or src/matrix.c
.src/matrix.h
and src/matrix.c
as you deem appropriate, but you may not change the function signatures in numc.h
and src/numc.c
.src/numc.c
.src/numc.c
, especially if you are not using a row-major setup for your matrices.dumbpy
library on hive as we have already installed it there for you!ssh into one of the hive machines under your cs61c class account. Visit visit https://galloc.cs61c.org/ and get your proj4 repository. Then, clone your repository locally and add the starter remote:
$ git clone YOUR_REPO_NAME
$ cd YOUR_REPO_NAME
$ git remote add starter https://github.com/61c-teach/sp21-proj4-starter.git
$ git pull starter master
If we publish changes to the starter code, retrieve them using git pull starter master
.
To be able install the modules that you will complete in this project, you must create a virtual environment with by running
$ python3.6 -m venv .venv
Note that you MUST use python 3.6 as our reference module dumbpy
only supports this specific version of python.
Finally, run the following command to activate the virtual environment:
$ source .venv/bin/activate
This will put you in a virtual environment needed for this project. Please remember that if you exit the virtual environment and want to return to work on the project, you must re-run source .venv/bin/activate
. This also means every time you re-ssh into the hive, you will have to re-run source .venv/bin/activate
.
Then, run
pip3 install -r requirements.txt
in the virtual environment. This will install all python packages you need for running your custom python tests.
Finally, if you have to exit out of the virtual environment, you can do so by running:
$ deactivate
We already have the reference library dumbpy
installed for you on Hive machines. You can import it with or without the virtual environment while using python3.6, and all object and function names are the same as the numc
module that you will implement (please refer to Task 3). You will only be able to access the dumbpy package on hive as we will not be directly releasing it. You can use it as a reference for both correctness and speed.
Again, for this project, we strongly suggest working on Hive machines in your cs61c class account. You will not be able to import dumbpy if you are using other class accounts. We may be unable to help you with issues caused by working outside of the Hive.
For this task, you will need to complete all functions in src/matrix.c
labelled with
/* TODO: YOUR CODE HERE */
. The comments above each function signature in src/matrix.c
contain instructions on how to implement the functions, so read them carefully before you start coding.
The matrix
struct is defined in src/matrix.h
. Feel free to change it, but make sure your changes are compatible with our starter code.
typedef struct matrix {
int rows;
int cols;
double* data;
int ref_cnt;
struct matrix *parent;
} matrix;
rows
is the number of rows of this matrix, cols
is the number of columns, and data
is a 1D representation of the 2D matrix data. ref_cnt
is the number of existing matrix
structs (including itself) that share all or part of the data array with this particular matrix
struct. parent
indicates whether this matrix
struct is a slice of another matrix, and should be set to its parent matrix
struct if it is and NULL
otherwise.
src/matrix.h
also imports the library Python.h
, but for this part you should not need any other functions besides PyErr_SetString
.
Depending on your implementation of src/matrix.c
and src/numc.c
, you may or may not assume that result
is already pre-allocated or that all inputs’ dimensions are valid. However, as mentioned in the Tips and Guidelines section, your Python number methods will need to handle the case where matrix allocation fails.
The function allocate_matrix_ref
is called from src/numc.c
’s Matrix61c_subscript
function and and is used for getting a row of the from
matrix (see Info: numc.Matrix indexing for an example). Currently, Matrix61c_subscript
and allocate_matrix_ref
assume a row-major setup. If you choose to implement your matrices as column-major, you will have to change the implementation of Matrix61c_subscript
, and you might also want to change the function signature of allocate_matrix_ref
.
Again, you may change any function signature in src/matrix.h
and src/matrix.c
.
Important notes:
allocate_matrix
or allocate_matrix_ref
fails to allocate space, or a value error if you are trying to allocate matrices with non-positive dimensions! Otherwise you would have to make sure that a runtime/value error will be thrown in src/numc.c
whenever we run out of memory. This includes throwing an error in Matrix61c_init.deallocate
function as well as the ref_cnt
field in the matrix
struct have caused a lot of confusions in the past semesters. It is important to remember that this ref_cnt
is NOT Python’s internal reference count. It is simply a field that will help you implement the deallocate
function. It does not have to reflect the true reference count if you deem that setting it to other values will simplify your implementation of deallocate
.deallocate
function, since there can be multiple matrices that refer to the same data array in the memory, you must not free the data until you call deallocate
on the last existing matrix that refers to that data. If you are having some difficulties implementing this, here’s a hint: you can keep the matrix struct in the memory even if you have already called deallocate
on that matrix. You only need to make sure to that the struct is freed once the last matrix referring to its data is deallocated.
We’ve provided some sanity in tests/mat_test.c
. These tests make several assumptions:
matrix
struct in src/matrix.h
get
and set
assume that your get
and set
are correctViolation of one or more of these assumptions may not cause your tests to fail, but please keep this in mind if your tests are failing and you are violating at least one of these assumptions.
To run the CUnit tests, run
$ make test
in the root folder of your project. This will create an executable called test
in the root folder and run it.
By default, CUnit will run these tests in Normal mode. When debugging a specific issue, it may be helpful to switch to Verbose mode, which can be done by commenting and uncommenting the relevant lines in mat_test.c
:
// CU_basic_set_mode(CU_BRM_NORMAL);
CU_basic_set_mode(CU_BRM_VERBOSE);
Make sure that one line is uncommented at a time.
Please keep in mind that these tests are not comprehensive, and passing all the sanity tests does not necessarily mean your implementation is correct. This is especially true with the memory functions allocate_matrix
, allocate_matrix_ref
, and deallocate_matrix
. Also keep in mind that the autograder will be using our own set of sanity tests, and will not be running your CUnit tests.
Another thing to note is that the Makefile
is written for compilation on the hive machines. If you wish to run it locally, you will have to modify the Makefile
by replacing the path to your CUnit/Python libraries in your CUNIT
and PYTHON
variables. You will also need to make sure that your local computer supports AVX extensions and OpenMP.
Finally, you are welcomed to modify the tests/mat_test.c
file in the tests
directory to implement your custom test cases.
The setup.py
file is used for installing your custom-built modules. After completing it, you should be able to install numc
by simply running:
$ make
This will uninstall your previously installed numc
module if it existed and reinstall numc
. We have written src/numc.c
so that numc.Matrix
will be initialized and ready to import upon succesful installation of the numc
module. You should rerun make
every time you make changes and want them to be reflected in the numc
module.
You can uninstall your numc
module by running
$ make uninstall
We have provided you with the compiler and linker flags in setup.py
, and your task is to find out how to use them to build your module.
You will likely get a lot of warnings about functions being defined but not used, and that’s ok! You should ignore these warnings for now, and they will be gone after you finish writing Task 3.
Remember that you must be in the virtual environment that you set up in order to install the modules, otherwise you will get a “Read-only file system” error.
READ FIRST: take a look at the function distutils.core.setup
(https://docs.python.org/3.6/distutils/apiref.html), and here is an example usage. You only need two function calls to complete this section, if you’re doing more than that, please reread the docs included as you’re likely doing something wrong
Now that you have successfully installed your numc
module, you can import your numc.Matrix
objects in Python programs! Here are some ready-to-use features already implemented for numc.Matrix
objects. You might find them helpful when debugging Task 3.
numc.Matrix
Here are several ways of importing numc.Matrix
from numc import Matrix
import numc
numc.Matrix
import numc as nc
nc.Matrix
numc.Matrix
initializationThe code block below lists all the different ways of creating a numc.Matrix
object.
>>> import numc as nc
CS61C Spring 2021 Project 4: numc imported!
>>> nc.Matrix(3, 3) # This creates a 3 * 3 matrix with entries all zeros
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
>>> nc.Matrix(3, 3, 1) # This creates a 3 * 3 matrix with entries all ones
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
>>> nc.Matrix([[1, 2, 3], [4, 5, 6]]) # This creates a 2 * 3 matrix with first row 1, 2, 3, second row 4, 5, 6
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
>>> nc.Matrix(1, 2, [4, 5]) # This creates a 1 * 2 matrix with entries 4, 5
[[4.0, 5.0]]
More specifically:
nc.Matrix(rows: int, cols: int)
will create a matrix with rows
rows and cols
cols. All entries in this matrix are defaulted to 0.nc.Matrix(rows: int, cols: int, val: int/float)
will create a matrix with rows
rows and cols
cols. All entries in this matrix will be initialized to val
.nc.Matrix(rows: int, cols: int, lst: List[int/float])
will create a matrix with rows
rows and cols
cols. lst
must have length rows * cols
, and entries of the matrix will be initialized to values of lst
in a row-major order.nc.Matrix(lst: List[List[int/float]])
will create a matrix with the same shape as the 2D lst
(i.e. each list in lst
is a row for this matrix).numc.Matrix
indexingYou can index into a matrix and change either the value of one single entry or an entire row. More specifically, mat[i]
should give you the i
th row of matrix
. If mat
has more than 1 column, mat[i]
should also be of type numc.Matrix
with (mat
’s number of columns, 1) as its shape. In other words, mat[i]
returns a column vector. This is to support 2D indexing of numc
matrices.
If mat
only has one column, then mat[i]
will return a double. mat[i][j]
should give you the entry at the i
th row and j
th column. If you are setting one single entry by indexing, the data type must be float or int. If you are setting an entire row of a matrix that has more than one column, you must provide a 1D list that has the same length as the number of columns of that matrix. Every element of this list must be either of type float or int.
Please note that if mat[i]
has more than 1 entry, it will share data with mat
, and changing mat[i]
will result in a change in mat
.
The example given below assumes the matrices are initialized from the code block above.
>>> import numc as nc
CS61C Spring 2021 Project 4: numc imported!
>>> mat = nc.Matrix([[1, 2, 3], [4, 5, 6]]) # This creates a 2 * 3 matrix with first row 1, 2, 3, second row 4, 5, 6
>>> mat
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
>>> slice = mat[0]
>>> slice
[[1.0], [2.0], [3.0]]
>>> slice[0]
1.0
>>> slice[1] = 10.0 # Change a value in slice
>>> slice
[[1.0], [10.0], [3.0]]
>>> mat # Mat is changed as well
[[1.0, 10.0, 3.0], [4.0, 5.0, 6.0]]
Partial slices, however, are not supported. For example,
mat[1:3] # not allowed
mat[0][1:3] # not allowed
The matrices and vectors have an attribute shape, which is a tuple of (rows, cols)
. Example is given below.
>>> mat.shape
(3, 3)
Here is the link to the full reference manual: https://docs.python.org/3.6/c-api/index.html. If you ever find anything confusing in the skeleton code or are at a lost on how to implement src/numc.c
, this is a great resource.
numc
skeleton codeWe define the Matrix61c
struct in numc.h
. It is of type PyObject
(this means you can always cast Matrix61c
to PyObject
, but not vice versa), which according to the official documentation, “contains the information Python needs to treat a pointer to an object as an object”. Our Matrix61c
has the matrix
struct we defined in src/matrix.h
.
Then we define a struct PyTypeObject
named Matrix61cType
to specify the intended behaviors of our Python object Matrix61c
. This struct will then be initialized to be our numc.Matrix
objects.
static PyTypeObject Matrix61cType = {
PyVarObject_HEAD_INIT(NULL, 0)
.tp_name = "numc.Matrix",
.tp_basicsize = sizeof(Matrix61c),
.tp_dealloc = (destructor)Matrix61c_dealloc,
.tp_repr = (reprfunc)Matrix61c_repr,
.tp_as_number = &Matrix61c_as_number,
.tp_flags = Py_TPFLAGS_DEFAULT |
Py_TPFLAGS_BASETYPE,
.tp_doc = "numc.Matrix objects",
.tp_methods = Matrix61c_methods,
.tp_members = Matrix61c_members,
.tp_as_mapping = &Matrix61c_mapping,
.tp_init = (initproc)Matrix61c_init,
.tp_new = Matrix61c_new
};
For example, .tp_dealloc
tells Python which function to call to destroy a numc.Matrix
object when its reference count becomes 0, and .tp_members
tells Python what instance attributes numc.Matrix
objects have. You can take a look at the official documentation if you are curious.
Here is a list of some functions and Python objects from <Python.h>
that you may find useful. You can also choose any other functions at this link.
Now you are ready to complete src/numc.c
, the Python-C interface! As before, you will need to fill out all functions and variables labeled /* TODO: YOUR CODE HERE */
. The code for initializing the module numc
and the object type numc.Matrix
is already done for you. Although not required, we encourage you to take a look at the existing code to better understand the interface.
Below are the two main parts for this task.
For this part, we ask you to overload operators for numc.Matrix
objects. Here are the expected behaviors of overloaded operators:
a + b
: Element-wise sum of a
and b
. Returns a numc.Matrix
object.
TypeError
if not both a
and b
are of type numc.Matrix
.ValueError
if a
and b
do not have the same dimensions.a - b
: Element-wise subtraction of a
and b
. Returns a numc.Matrix
object.
TypeError
if not both a
and b
are of type numc.Matrix
.ValueError
if a
and b
do not have the same dimensions.a * b
: Matrix multiplication of a
and b
. Returns a numc.Matrix
object. Remember that this is a matrix multiplication, not an element-wise multiplication.
TypeError
if not both a
and b
are of type numc.Matrix
ValueError
if a
’s number of columns is not equal to b
’s number of rows.-a
: Element-wise negation of a
. Returns a numc.Matrix
object.abs(a)
: Element-wise absolute value of a
. Returns a numc.Matrix
object.a ** pow
: Raise a
to the pow
th power. a
to the 0th power is the identity matrix (1 on the top left to bottom right diagonal and 0 everywhere else). Returns a numc.Matrix
object. This operator is defined in terms of matrix multiplication, not element-wise multiplication.
TypeError
if a
is not of type numc.Matrix
or pow
is not an integer.ValueError
if a
is not a square matrix or if pow
is negative.Please note that for all these operations above, you never directly modify the matrix that you pass in. You always make a new numc.Matrix
object to hold your result, so make sure you set the shape
attribute of the new numc.Matrix
. You can use Matrix61c_new
to create new numc.Matrix
objects. Take a look at the implementation of Matrix61c_subscript
for an example.
For all the functions above, throw a runtime error if any error occurs (such as matrix allocation failure) and causes the operation to fail. Moreover, for any operations that involve two instances of numc.Matrix
, you will have to make sure that both a
and b
are indeed of type numc.Matrix
as we do not support operations between numc.Matrix
and other data/object types. Please read the comments in src/numc.c
carefully.
Here is a table that tells you which function in src/numc.c
in which you will implement each of the above operators
Operator | Function |
---|---|
+ | Matrix61c_add |
- (subtraction) | Matrix61c_sub |
* | Matrix61c_multiply |
- (negation) | Matrix61c_neg |
abs() | Matrix61c_abs |
** | Matrix61c_pow |
All these functions will be called through a Python-C interface after you complete the numc
module. In other words, these are the functions that will be called when you do matrix operations with numc.Matrix
objects, and these interface methods will call src/matrix.c
methods that you just implemented. You will have to check for the validity of the dimensions before actually carrying out the arithmetic, and throw an error if needed. Specifically, throw an type error if the arguments’ dimensions are invalid, and a runtime error if any memory allocation fails during execution. Again, depending on your implementation, these error checks could either be in src/matrix.c
or src/numc.c
After you implement all the functions above, you will need to fill out the struct Matrix61c_as_number
, which is used to define the object type numc.Matrix
. Remember to cast your functions to the correct types when assigning them to Matrix61c_as_number
’s fields!
Here is the link to the official documentation of a PyNumberMethods
struct: https://docs.python.org/3/c-api/typeobj.html#c.PyNumberMethods
You will implement two instance methods for numc.Matrix
:
set(self, i, j, val)
: Set self
’s entry at the i
th row and j
th column to val
.
TypeError
if the number of arguments parsed from args is not 3, if i
and j
are not integers, or if val
is not a float or int.IndexError
if i
or j
or both are out of range.get(self, i, j)
: Returns the entry at the i
th row and j
th column. Throw a type error if the number of arguments parsed from args is not 2 or if the arguments are of the wrong types. Throw an index error if either i
, j
, or both are out of range. Return value is a Python float.
TypeError
if the number of arguments parsed from args is not 2 or if either i
or j
is not an integer.IndexError
if i
or j
or both are out of range.
These functions will call get
and set
in src/matrix.c
to actually get or set the value. Again, you can throw errors either in src/numc.c
or src/matrix.c
.Here is a table that tells you which functions in src/numc.c
in which you will implement each of the above instance methods
Python method | C Function |
---|---|
set |
Matrix61c_set_value |
get |
Matrix61c_get_value |
After you implement all the functions above, you will need to fill out the array of PyMethodDef
structs Matrix61c_methods
, which is used to define the object type numc.Matrix
.
This link tells you what goes into a PyMethodDef
struct: https://docs.python.org/3/c-api/structures.html
As mentioned in task 1, if you are storing your matrix data in a non-row-major order, you might want to change your Matrix61c_subscript
.
Regardless of how you are storing you matrices, now is a good time to check if your allocate_matrix_ref
in src/matrix.c
is working as intended. A correct implementation of allocate_matrix_ref
and Matrix61c_subscript
should result in behaviors specified in the indexing info section above. More importantly, please take some time to make sure that you don’t have memory leaks! In other words, you need to make sure that when all references to a matrix is gone, that matrix’s data need to be free’d immediately. However, when the parent itself is gone but there are still existing slices of this matrix, you can delay the freeing of the parent’s data until all those slices are also gone. Here’s an example to what is meant by the above.
import numc as nc
a = nc.Matrix(2, 2)
b = a[0] # b is referencing a's data
del a # a is gone, but you don't have to free a's data right now
del b # b is gone, now you need to free a's data
To debug the Python-C interface, we suggest that you write your test files in Python, and use gdb or both gdb and pdb to debug.
You don’t have to use pdb if you do not wish to set breakpoints in your Python test file. Open your terminal and run
$ gdb python3
Then you can set breakpoints in your C files using the normal gdb commands. gdb will warn you with
No source file named {your c file}
Make breakpoint pending on future shared library load? (y or [n])
Press ‘y’ (without the quotes) to proceed.
After that, you can run run {your python test file name}.py
in gdb, and gdb will break at the breakpoints that you just set.
You will have to use pdb if you wish to set breakpoints in your Python file. Here’s how it works. Start gdb by running
$ gdb python3
and set your breakpoints in C (see previous section). Then you will need to run in gdb
run -m pdb {your python file}.py
After this step, you can set breakpoints in your Python file using gdb syntax (for example, b test.py:5
). With this approach, your debugger will switch between pdb and gdb depending on whether you are stepping through a Python file or a C file. If you are seeing values being optimized out on gdb, try changing O3
to O0
in setup.py
, then reinstalling your module.
Now that you have completed the three steps above and successfully installed your naive version of numc
, it’s time to speed up your matrix functions in src/matrix.c
! Below we outline some steps for boosting performance.
You should first try to speed up the computation by trying to apply conventional code optimizations (i.e. without using SSE or OpenMP). While we won’t tell you the exact steps, here are some hints that should help you get started:
Note that the above hints relate to general optimization practices. You do not necessarily need to do all of these to achieve a good speedup.
Once you have improved performance using these optimizations, you can start applying vectorization and parallelization to make the program even faster. Note that you have considerable freedom to apply any of these optimizations, and there is more than one correct solution. Try to experiment with different approaches and see which one gives you the best performance.
From lectures, you learned how to apply SIMD instructions to improve performance. The processors in the hive machines support the Intel AVX extensions, which allow you to do SIMD operations on 256 bit values (not just 128 bit, as we have seen in the lab). You should use these extensions to perform four operations in parallel (since all floating point numbers are doubles, which are 64 bit in size). If you are unfamiliar with SIMD instructions, lab 9 can be a good warmup.
As a reminder, you can use the Intel Intrinsics Guide as a reference to look up the relevant instructions. You will have to use the __m256d
type to hold 4 doubles in a YMM register, and then use the _mm256_*
intrinsics to operate on them.
Here is a list of AVX instructions that you may find helpful, although you are also allowed to use other AVX instructions not on the list.
void _mm256_storeu_pd (double * mem_addr, __m256d a)
__m256d _mm256_set1_pd (double a)
__m256d _mm256_set_pd (double e3, double e2, double e1, double e0)
__m256d _mm256_loadu_pd (double const * mem_addr)
__m256d _mm256_add_pd (__m256d a, __m256d b)
__m256d _mm256_sub_pd (__m256d a, __m256d b)
__m256d _mm256_fmadd_pd (__m256d a, __m256d b, __m256d c)
__m256d _mm256_mul_pd (__m256d a, __m256d b)
__m256d _mm256_cmp_pd (__m256d a, __m256d b, const int imm8)
__m256d _mm256_and_pd (__m256d a, __m256d b)
__m256d _mm256_max_pd (__m256d a, __m256d b)
Finally you should use OpenMP to parallelize computation. Note that you will need to make sure that none of the different threads overwrites each others’ data. Just adding a #pragma omp parallel for
may cause errors.
Note that the Hive machines have 4 cores with two hyperthreads each. This means that you should expect a speed-up of 4-8x (note that hyperthreads mean that two different threads execute on the same physical core at the same time; they will therefore compete for processor resources, and as a result, you will not get the same performance as if you were running on two completely separate cores).
Write up what you did in your README.md! While we do not have a specific format we are looking for, you should discuss what you did as a whole, the different python functions you implemented, what performance improvements you had, what were you surprised about, etc. More specifically, we want you to document your numc
module and provide example usages. We expect a minimum of 3000 characters. This may seem like a lot, but documenting you’ve done actually takes quite a number of words, so just sufficiently covering all the things you did in the project should get you to the word count requirement.
Again, failure to complete this task may result in negative points, so make sure you do it or you will lose points!
We will not be grading your tests but we will NOT help you debug unless you have written a test which shows how your code is failing. This means just using the autograder to figure out your issues will not be acceptable for office hours.
We use tests/unittests
as the framework for testing and have provided a tests/unittests
folder that contains this framework for testing your python module. You should be familiar with unittest
by now as you have had experience with it in project 2. Here is the official documentation for the standard Python unittest library.
tests/unittests/unittests.py
contains all the skeleton code for both correctness and performance tests, and tests/unittests/utils.py
has all the functions that you might need in order to write tests. We have provided some sample tests for you, but it is up to you to design and code up the tests. We will not be grading your tests.
As mentioned in Tips and Guidelines and Getting Started, we have installed the naive solution which we will be comparing against on hive! The python package is called dumbpy
and you can import it like any other python library (so long as you are on hive)! Please note we will not be distributing this binary which means you must work on hive if you want to test with it. You should use this and the time
package to determine how much you sped up your code.
tests/unittests/utils.py
dp_nc_matrix(*args, **kwargs)
dumbpy
matrix and a numc
matrix that are identical. *args
and **kwargs
will be used as the arguments to instantiate both matrices in the exact same format as you would instantiate a numc.Matrix
. We provide some examples below.
>>> dp_mat, nc_mat = dp_nc_matrix(2, 2, 0)
>>> dp_mat
[[0.0, 0.0], [0.0, 0.0]]
>>> nc_mat
[[0.0, 0.0], [0.0, 0.0]]
>>> dp_mat, nc_mat = dp_nc_matrix([[1, 2, 3]])
>>> dp_mat
[[1.0, 2.0, 3.0]]
>>> nc_mat
[[1.0, 2.0, 3.0]]
rand_dp_nc_matrix(rows, cols, low=0, high=1, seed=0)
dumbpy
matrix and a random numc
matrix with seed
where each element is in the range [low, high). The two matrices are identical with rows
rows and cols
. seed
is defaulted to 0.
>>>> dp_mat, nc_mat = rand_dp_nc_matrix(2, 2, seed=5)
>>> dp_mat
[[0.27474559623503386, 0.046467764790387715], [0.9927552244592249, 0.08003044504673706]]
>>> nc_mat
[[0.27474559623503386, 0.046467764790387715], [0.9927552244592249, 0.08003044504673706]]
cmp_dp_nc_matrix(dp_mat: dp.Matrix, nc_mat: nc.Matrix)
dp_mat
has the same size of nc_mat
and all corresponding elements are equal (within a margin of error).compute(dp_mat_lst: List[Union[dp.Matrix, int]],
nc_mat_lst: List[Union[nc.Matrix, int]], op: str)
op
on these matrices. Note that op
is a string. "add"
, "sub"
, "mul"
, "neg"
, "abs"
, and "pow"
correspond to the operations +
, -
(subtraction), *
, -
(negation), and abs
, respectively. This function will return whether the computed dumbpy
matrix is equal to the computed numc
matrix, as well as the speedup for this specific computation.
"neg"
and "abs"
, each matrix list must only contain 1 matrix. For the binary operations, they must contain more than 1 matrix."pow"
, dp_mat_lst
and nc_mat_lst
should look like something like [Matrix(2, 2), 1]
.compute([a, b, c], [d, e, f], "add")
where a, b, c, d, e, f
are matrices, and the function will compute a + b + c
and d + e + f
.unittest
$ python -m unittest unittests.py -v
will run all tests in unittests.py
.
$ python -m unittest unittests.{classname} -v
For example, running
$ python -m unittest unittests.TestAdd -v
will only run the tests under the TestAdd
class.
$ python -m unittest unittests.{classname}.{testname} -v
For example, running
$ python -m unittest unittests.TestAdd.test_small_add -v
will only run the test_small_add
test.
make test
, what is happening?
rm -f test
gcc -g -Wall -std=c99 -fopenmp -mavx -mfma -pthread -O3 mat_test.c src/matrix.c -o test -fopenmp -L/home/ff/cs61c/cunit/install/lib -I/home/ff/cs61c/cunit/install/include -lcunit -I/usr/include/python3.6 -lpython3.6m
./test
CUnit - A unit testing framework for C - Version 2.1-2
http://cunit.sourceforge.net/
Makefile:25: recipe for target 'test' failed
make: *** [test] Illegal instruction (core dumped)
A 1.2: This means your code segfaulted before any assert statements was reached and none of the tests passed. Use gdb to locate your bug.
mul_matrix(mat, mat, mat)
../test
? mat1 = [[1,2,3],[4,5,6]]
, mat2 = mat1[0]
. Can we do mat3 = mat2[0]
?mat3
will just be a number and changing its value will not change mat2
.undefined symbol: allocate_matrix_ref
). What is happening?deallocate_matrix
implementation. What happens when you call print(mat[0])
is that it will create a slice, then immediately deallocate it after this line.<class 'AttributeError'> -- shape
.mat->shape
<class 'SystemError'> -- <built-in method set of numc.Matrix object at 0x7f175faffef0> returned NULL without setting an error!
NULL
in any interface function.UnicodeDecodeError: ‘utf-8’ codec can't decode byte 0x89 in position 5: invalid start byte
upon import. What’s wrong?{NULL, NULL, 0, NULL}
from starter code must be last element of Matrix61c_methods
, so make sure you don’t accidentally remove that!src/matrix.c
?PyObject* args
?PyLong_AsLong
as int?Matrix61c_multiply
cover both nb_matrix_multiply
and nb_multiply
, or just nb_multiply
?nb_multiply
. nb_matrix_multiply
corresponds to the operator @
.dumbpy
and numc
matrices are exactly the same, however, comparing them using ==
returns False.cmp_dp_nc_matrix
, which is provided in unittests/utils.py
. #pragma omp parallel for
above a simple for loop slows down my add_matrix drastically?warning: ignoring #pragma omp parallel [-Wunknown-pragmas]
but I have not changed the Makefile?shape
attribute of every returned matrix!abs_matrix
logic is very simple.abs
function. If so, you probably want to manually write another one since the built-in abs
function will round off all decimal places. Q6.1: Why are there TODO: YOUR CODE HERE
comments in the test functions in unittests.py
even though these functions have already been implemented for us?
A6.1: The tests we have provided are VERY simple and they are purely examples to help you write more tests. Please modify these tests or add more tests for more comprehensive testing.
Q6.2: My tests are failing inside rand_md5
, is this a bug from the starter code?
A6.2: No. Make sure your implementations Matrix61c_get_value
and Matrix61c_get_value
are correct! Otherwise rand_md5
might error.
The grading breakdown for Project 4 is as follows:
**Updated on April 20th: ** Here are the graphs for the speedup tests. The x-axis is your speedup times and the y-axis indicates what percentage of that test’s total score you will receive.
Multiplication | Power |
---|---|
Simple | Comprehensive |
---|---|
Minimum speedup for 100% on each test:
Since we are running your submissions on hive, albeit reserved, speedup times may fluctuate a bit. You should try to go above the speedup value as we will rerun the ag after the deadline and your speedup may go up or down. We will not rerun submissions if they went down unless we made another change to the autograder.
We will only be using your src/matrix.h
, src/matrix.c
, src/numc.c
, setup.py
, and README.md
for grading. Gradescope only shows you all the core correctness tests that are needed to run performance tests. Other tests will stay hidden until the late due date. Keep in mind that your Matrix61c_get
method in src/numc.c
as well as the shapes you set for your matrices need to be correct in order for you to pass any of those correctness tests.