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Assignment 7
CSCI 2132: Software Development
Due April 8, 2019
Assignments are due on the due date before 23:59. All assignments must be submitted electronically via the course SVN
server. Plagiarism in assignment answers will not be tolerated. By submitting your answers to this assignment, you declare
that your answers are your original work and that you did not use any sources for its preparation other than the class notes,
the textbook, and ones explicitly acknowledged in your answers. Any suspected act of plagiarism will be reported to the
Faculty’s Academic Integrity Officer and possibly to the Senate Discipline Committee. The penalty for academic dishonesty
may range from failing the course to expulsion from the university, in accordance with Dalhousie University’s regulations
regarding academic integrity.
General Instructions: How to Submit Your Work
You must submit your assignment answers electronically:
Change into your subversion directory on bluenose: cd ~/csci2132/svn/CSID .
Create a directory a7 for the current assignment.
Change into your assignment directory: cd a7 .
Create files inside the a7 directory as instructed in the questions below and put them under
Subversion control using svn add <filename> . Only add the files you are asked to add!
Once you are done answering all questions in the assignment (or the ones that you are able to
answer—hopefully all), the contents of your a7 directory should look like this:
a7
permutations.c
linked_list.c
linked_list.h
node_pool.c
node_pool.h
stringset.c
(You will also have executable programs and potentially some data files in this directory, but you
should not add them to SVN.) Submit your work using svn commit -m”Submit Assignment 7″ .
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(Q1) Recursion
Consider the sequence of the first n positive integers, ?1,2, . . . , n?. A permutation of this sequence is
any sequence
that contains each of the numbers 1, 2, . . . , n exactly once. So, ?3, 4, 2,
is a permutation of 1, 2, 3, 4 but 1, 3, 2, 3 and 1, 5, 3, 2 are not.
A permutation x1
is lexicographically less than another permutation y1 yn
if and
only if there exists an index 1 ≤ k ≤ n such that
xi = yi
for all 1 ≤ i < k and xk < yk.
So,2, 1, 3, 4 is lexicographically less than 2, 3, 1, 4 because x1 = y1 = 2 and x2 = 1 < y2 = 3. If the
elements in the sequence were letters rather than integers, you could interpret the sequence of letters
as a word and the ordering defined here would simply be the one in which the words would occur in a
dictionary.
Your task is to write a C program permutations that takes a command line argument n and outputs
all permutations of the sequence ?1,2, . . . , n? in lexicographic order. The output should consist of
n! lines, each containing one permutation. The numbers in each permutation must be printed in
order, separated by exactly one space between each pair of consecutive numbers. Thus, when running
permutations 3, you should see the following output on your screen:
$ ./permutations 3
1 2 3
1 3 2
2 1 3
2 3 1
3 1 2
3 2 1
permutations 4 should produce
$ ./permutations 4
1 2 3 4
1 2 4 3
1 3 2 4
1 3 4 2
1 4 2 3
1 4 3 2
2 1 3 4
2 1 4 3
2 3 1 4
2 3 4 1
2 4 1 3
2 4 3 1
3 1 2 4
3 1 4 2
2
3 2 1 4
3 2 4 1
3 4 1 2
3 4 2 1
4 1 2 3
4 1 3 2
4 2 1 3
4 2 3 1
4 3 1 2
4 3 2 1
As the title of the question suggests, recursion is a natural strategy to implement this program. You
should not have to generate the permutations and then sort them. Your code should be able to generate
the permutations in the desired order.
The basic strategy is fairly simple: Your top-level invocation of the function that generates the permutation
should iterate over all possible choices of the first element in the permutation, from smallest
to largest. For each such choice, you make a recursive call that generates all permutations of the
remaining n 1 elements. How do you do this? You iterate over all possible choices for the second
element, from smallest to largest and, for each such choice, make a recursive call that generates all
permutations of the remaining n?2 elements, and so on. The deepest recursive call is asked to generate
a permutation of 0 elements, that is, you have already chosen all n elements in the permutation. This
is the point at which you print the current permutation.
One issue you will have to deal with is how to keep track of the elements that can still be chosen as
the second, third, fourth, . . . element in the permutationu after choosing the first one, two, three, . . .
elements in the permutation. One easy way to do this is to initialize an array of size n that contains
all the numbers from 1 to n. Whenever you choose an element from this array, you set its entry to 0
and, in subsequent recursive calls, you skip elements that are 0 because they were already chosen.
Don’t forget to restore each number x in this array of candidates when it is no longer part of the set of
elements currently chosen as part of the permutation. This is not the most efficient way to do this, but
efficiency is not your main concern in this question.
Implement your program in a single source file permutations.c. Do not submit the executable file
obtained by compiling it. However, you should obviously compile and test your code.
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(Q2) Structs, unions, and dynamic memory management
The ability to allocate and deallocate memory on the heap at arbitrary times is key to writing flexible
programs, but every allocation or deallocation has an associated cost because the runtime system or
operating system needs to perform a certain amount of bookkeeping to keep track of which memory
locations on the heap are currently occupied and which ones are available for future allocation requests.
As a result, in programs that create and destroy many small objects on the heap, the cost of heap
management may become the main performance bottleneck. This is the case in object-oriented programs
that manipulate complex collections of small objects and in the implementation of pointer-based data
structures such as linked lists and binary search trees, where a na?ve implementation represents each
list or tree node as a separately allocated block on the heap.
A common technique to improve the performance of such programs substantially is to allocate larger
chunks of memory at a time, large enough to hold hundreds or thousands of the manipulated objects
(e.g., a chunk that is big enough to hold 1000 list nodes in a linked list implementation). Your code
then has to manually track which of the slots in this large memory chunk are occupied or not, but this
is a much simpler problem that can be solved much more efficiently than general heap management.
The data structure that manages the allocation of individual list nodes from this array of list nodes is
often referred to as a“node pool”.
Here, your task is to implement a linked list that uses a node pool to efficiently manage the memory
occupied by its nodes. As an application, you will use the same test program as in Lab 8, which needs
a data structure to store a collection of strings. In Lab 8, you implement this collection as a splay tree
in order to guarantee fast searches. Here, you do not worry about the speed of searching the data
structure, only about the cost of heap management, so a linked list suffices as the data structure to
store the collection of strings.1
Develop your code for this question in four steps:
- Implement a basic linked list data structure. (You can use the discussion of linked lists from class
as a guide.) - Implement the test application. (You can cut and paste most of this code from Step 7 in Lab 8.)
- Implement the node pool.
- Modify your list implementation from Step 1 to use the node pool from Step 3 to manage the
allocation of list nodes.
Step 1: Implement a (singly) linked list
A (singly) linked list is a sequence of nodes linked together by successor pointers. The first node of
the list is its head. The last node is its tail. Every node that is not the tail has exactly one successor.
Every node that is not the head is the successor of exactly one node. Every node stores some data item,
which we will call the value of the node here. This is illustrated in Figure 1.
1
If you enjoy implementing data structures and are keen to practice your C programming skills, I encourage you to
attempt the following project in your spare time: The splay tree implementation in Lab 8 allocates each node as its own
block of memory on the heap. The linked list implementation you develop in this assignment avoids this but does not
support fast searches. Try to implement a splay tree (or other balanced binary search tree if you want) and use the strategy
from this assignment to manage the space occupied by its nodes efficiently.
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Head 5 3 11 1 7 NULL
Figure 1: A linked list storing a sequence of integers.
Develop a linked list implementation that supports the following operations:
list_t make_list();
This creates a new linked list and returns a reference to it. Similarly to Lab 8, the list should be
represented as a struct _list_t and list_t should be a pointer to a heap-allocated struct _list_t,
that is, the list_t type should be defined using typedef struct _list_t *list_t;
void destroy_list(list_t);
This destroys the given linked list, that is, it frees all the memory the list occupies on the heap. This
requires the deallocation of the list structure itself as well as of all node structures that are part of the
list.
node_t list_add(list_t, void *);
This adds a data item referenced by a void * to the beginning of the list. The list should simply store
the pointer to the data item in the new list node. The data should not be copied into the list. This
is similar to the approach taken in Lab 8. See the discussion there to understand why this is useful.
The return value is a pointer to the new list node, that is, the node_t type should be defined using
typedef struct _node_t *node_t;
void *list_delete(list_t, node_t);
This deletes the given node from the given list and returns the data that was stored at that node. (Since
the node only stores a void * to the data and the data is always kept externally from the list, you
simply return the void * that was stored at the list node.) Again, this is similar to the binary tree
implementation in Lab 8.
void *node_value(node_t);
This returns a pointer to the data element stored at the given node. (The node itself stores a void * to
this data item. You should simply return this pointer.)
node_t list_head(list_t);
This returns (a pointer to) the first node of the list. (Recall that node_t is a pointer to a list node.)
node_t node_succ(node_t);
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This returns the successor of the given node in the list, that is, the next node in the list after this node.
If the given node is the last node of the list, this function returns NULL.
In this first implementation of your linked list data structure, it is okay to allocate the list and the nodes
of the list as individual chunks of memory, that is, using one malloc call per list node.
For this step, create a header file linked_list.h that declares the above types and function prototypes
and an implementation file linked_list.c that defines the above types and functions. Review Steps
1–6 of Lab 8 if you do not remember how to do this.
Step 2: Use the linked list to store a collection of strings
For this step, you can use the code from Step 7 in Lab 8, but you have to modify it slightly because the
linked list structure you have developed in Step 1 does not support a search operation as the binary
search tree developed in Lab 8 does.
Your goal is to implement a program that starts out with an empty collection of strings and repeatedly
prompts you for input by printing“”. Valid inputs are:
a <string> to add a string to the collection.
d <pattern> to delete a string from the collection. This operation deletes the first string it
finds that starts with the given pattern. For example, d Nor may delete the string Norbert
from the collection if it is currently in the collection. If there is no string in the collection that
starts with the given pattern, this operation does not modify the current collection and prints
STRING NOT FOUND.
f <pattern> to find an arbitrary string in the collection that starts with the given pattern and
print it on the screen. Similar to the delete operation, f Nor may report the string Norbert if
this string is in the collection. Also similar to the delete operation, this find operation prints
STRING NOT FOUND if there is no string in the collection that starts with the given prefix.
l to list all strings currently in the collection. (In contrast to Lab 8, the strings do not need to be
listed in sorted order. Thus, you can simply list them in the order they are stored in the list.)
q to terminate the program.
Implement the required code (which you are free to copy from Lab 8 and modify appropriately) in a file
stringset.c. If you copy this code from Lab 8, you need to make sure that you include linked_list.h
instead of tree.h and that you replace the use of tree_find in the code from Lab 8 with appropriate
custom code that uses list_head, node_succ, and node_value to search for a string with the given
prefix as part of the delete and find operations. Additional small changes are required to replace other
calls to tree functions with their appropriate counterparts in the linked list implementation.
Building an executable program stringset requires you to compile both the stringset.c and
linked_list.c files and link the two resulting object files. Review Step 8 of Lab 8 if you do not
remember how to do this. This executable file is not part of your assignment submission, but you
should obviously build and run your code in order to check your code for syntax errors and test your
code.
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Step 3: Implement a node pool
This is the main creative part of this question. You will implement a node pool that allows you to
manage the heap memory occupied by list nodes more efficiently than by using one call to malloc per
list node.
The data structure you need to implement is implemented as a struct _node_pool_t. You will again
define an alias node_pool_t for pointers to this struct:
typedef struct _node_pool_t *node_pool_t;
The structure supports exactly four operations:
node_pool_t make_node_pool();
This creates a new node pool data structure.
void destroy_node_pool(node_pool_t);
This destroys the given node pool, that is, it frees all memory it occupies on the heap.
node_t request_node(node_pool_t);
This requests a new linked list node from the node pool. In Step 4, you will modify your linked list
implementation to replace calls to malloc to create new list nodes with calls to request_node.
void release_node(node_pool_t, node_t);
This releases a previously requested linked list node back to the pool. The node pool can reuse the
memory occupied by this node to satisfy future node requests made by calling request_node. In Step 4,
you will modify your linked list implementation to replace calls to free to release list nodes with calls
to release_node.
Let us discuss a strategy for developing an efficient node pool structure that does not call malloc for
every single list node that is created and instead requests space for list nodes in larger chunks.
Let us assume we will never create more than 1023 list nodes. (As a bonus part of this step, you may
remove this assumption.) Then we can get away with a single call to malloc by allocating a block
of memory of size 1023 * sizeof(struct _node_t), large enough to hold 1023 list nodes. This is
essentially an array of 1023 list node structs. The node pool then returns pointers to available slots in
this array in response to request_node calls.
But how does the node pool keep track of which array slots are available? It keeps a linked list of
available array slots! The slots are used themselves to store the pointers necessary to form this linked
list. Specifically, you should implement a slot as:
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union slot_t {
struct _node_t node;
slot_t *next;
};
This allows you to use the memory of the slot to store either a list node (when it is reserved for this
purpose by returning it to the user in response to a request_node call) or a pointer to the next available
array slot if this slot is currently unoccupied, that is, it is part of the linked list of available array slots.
The _node_pool_t implementation could then look like this:
struct _node_pool_t {
union slot_t slots[1023];
slot_t *free_slots;
};
The make_node_pool function has to allocate memory for this structure (using malloc) and initialize
the list of available slots by setting up pointers between them. The destroy_node_pool function simply
frees the memory allocated to the node pool. The request_node function returns the first available slot
in the list of slots starting with free_slots and updates free_slots to point to the next available slot
in this list. You should return NULL if the list of available slots is empty. The release_node function
makes the released node part of the list of free slots again by casting it to a slot_t *, setting its next
pointer to point to the current first slot in the list of available slots, and updating free_slots to point
to the returned slot.
For this step, create a header file node_pool.h that declares the above types and function prototypes
and an implementation file node_pool.c that defines the above types and functions.
Bonus: For 25% bonus marks, you should support allocating an arbitrary number of list nodes, not
just a fixed number. The strategy is as follows: Instead of allocating an array capable of holding 1023
list nodes, you start out with an empty node pool that does not have any space reserved for list nodes.
The free_slots pointer should be NULL to indicate that currently no slots are available. Then, the
request_node function should do one of two things:
If free_slots != NULL, then it should reserve the next available slot as in the above implementation.
If free_slots == NULL, which can happen at any time when all available slots are occupied, it
should allocate a new array of 1023 slots, form a new list of available slots using these slots, and
then return the first slot in this list.
This creates a small problem: When you destroy the pool, you need to free the memory occupied by all
1023-slot blocks you allocated. The strategy you should use to deal with this is to replace the slots
array with a linked list of structs, each holding one of the 1023-slot blocks. You can then free the
memory allocated by these blocks by traversing this linked list.
(This implementation has another problem: since you never free node blocks until the entire node
pool is destroyed, even if none of the slots in a given block is occupied, the total space used by the
node pool is the maximum number of nodes in the list over the lifetime of the list, rounded to the next
8
multiple of 1024. Thus, you may end up in a situation where a single peak usage that requires millions
of nodes makes your list occupy a lot of space even though the list stores only a few hundred nodes
most of the time. There are ways to mitigate this while keeping the cost per list operation constant,
but this is well beyond the scope of this course.)
Step 4: Use the node pool in your linked list implementation
Modify your linked list implementation from Step 1 to use the node pool from Step 3 to manage the
heap memory occupied by list nodes. This requires the following simple changes:
You need to modify your _list_t structure so it also stores a reference of type node_pool_t to a
node pool to be used by this list.
Your make_list function should call make_node_pool to create a new node pool to be used by
this list.
Your destroy_list function also needs to destroy the node pool used by the given list by calling
destroy_node_pool. If you think about it, you no longer need to release individual nodes as a
result when destroying the list.
All other operations that create or destroy list nodes should use request_node and release_node
instead of malloc and free to create and destroy list nodes.
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