Fabián E. Bustamante, Spring 2007 Virtual Memory Today Motivations for VM Address translation Accelerating translation with TLBs Dynamic memory allocation.

Fabián E. Bustamante, Spring 2007

Virtual Memory

Today Motivations for VM Address translation Accelerating translation with TLBs Dynamic memory allocation – mechanisms & policies Memory bugs

A system with physical memory only

Addresses generated by the CPU correspond directly to bytes in physical memory

EECS 213 Introduction to Computer SystemsNorthwestern University

2

CPU

0:1:

N-1:

Memory

PhysicalAddresses

E.g. most Cray machines, early PCs, nearly all embedded systems, etc.

A system with virtual memory

Modern processors use virtual addresses

Hardware converts virtual addresses to physical addresses via OS-managed lookup table (page table)


3

CPU

0:1:

N-1:

Memory

0:1:

P-1:

Page Table

Disk

VirtualAddresses

PhysicalAddresses

E.g. workstations, servers, modern PCs, etc.


4

Motivations for virtual memory

Use physical DRAM as a cache for the disk– Address space of a process can exceed physical memory

size– Sum of address spaces of multiple processes can exceed

physical memory

Simplify memory management– Multiple processes resident in main memory.

• Each process with its own address space

– Only “active” code and data is actually in memory• Allocate more memory to process as needed.

Provide protection– One process can’t interfere with another.

• because they operate in different address spaces.

– User process cannot access privileged information• different sections of address spaces have different permissions.


5

Motivation #1: DRAM a “cache” for disk

Full address space is quite large:– 32-bit addresses: ~4,000,000,000 (4 billion) bytes– 64-bit addresses: ~16,000,000,000,000,000,000 (16

quintillion) bytes

Disk storage is ~300X cheaper than DRAM storage– 80 GB of DRAM: ~ $33,000– 80 GB of disk: ~ $110

To access large amounts of data in a cost-effective manner, the bulk of the data must be stored on disk

1GB: ~$200 80 GB: ~$110

4 MB: ~$500

DiskDRAMSRAM


6

Levels in memory hierarchy

CPUregs

Cache

Memory disk

size:speed:$/Mbyte:line size:

32 B1 ns

8 B

Register Cache Memory Disk Memory

32KB-4MB2 ns$125/MB32 B

1024 MB30 ns$0.20/MB4 KB

100 GB8 ms$0.001/MB

larger, slower, cheaper

8 B 32 B 4 KB

cache virtual memory

DRAM vs. SRAM as a “cache”

DRAM vs. disk is more extreme than SRAM vs. DRAM– Access latencies:

• DRAM ~10X slower than SRAM

• Disk ~100,000X slower than DRAM

– Importance of exploiting spatial locality:• First byte is ~100,000X slower than successive bytes on disk

– vs. ~4X improvement for page-mode vs. regular accesses to DRAM

– Bottom line: • Design decisions made for DRAM caches driven by enormous

cost of misses


7

Impact of properties on design

If DRAM was to be organized similar to an SRAM cache, how would we set the following design parameters?– Line size? Large, since disk better at transferring large blocks– Associativity? High, to minimize miss rate– Write through or write back?

• Write back, since can’t afford to perform small writes to disk

What would the impact of these choices be on:– Miss rate: Extremely low. << 1%– Hit time: Must match cache/DRAM performance– Miss latency: Very high. ~20ms– Tag storage overhead: Low, relative to block size


8

Locating an object in a “Cache”

SRAM Cache– Tag stored with cache line– Maps from cache block to memory blocks

• From cached to uncached form

• Save a few bits by only storing tag

– No tag for block not in cache– Hardware retrieves information

• Can quickly match against multiple tags


9

X

Object Name

Tag Data

D 243

X 17

J 105

•••

•••

0:

1:

N-1:

= X?

“Cache”

Locating an object in “Cache” (cont.)

DRAM Cache– Each allocated page of virtual memory has entry in page table– Mapping from virtual pages to physical pages

• From uncached form to cached form

– Page table entry even if page not in memory• Specifies disk address

• Only way to indicate where to find page

– OS retrieves information


10

Data

243

17

105

•••

0:

1:

N-1:

X

Object Name

Location

•••

D:

J:

X: 1

0

On Disk

“Cache”Page Table

Page faults (like “cache misses”)

What if an object is on disk rather than in memory?– Page table entry indicates virtual address not in memory– OS exception handler invoked to move data from disk into

memory• current process suspends, others can resume

• OS has full control over placement, etc.


11

CPU

Memory

Page Table

Disk

VirtualAddresses

PhysicalAddresses

CPU

Memory

Page TableVirtual

AddressesPhysical

Addresses

Before fault After fault

Disk

Servicing a page fault

Processor signals controller– Read block of length P

starting at disk address X and store starting at memory address Y

Read occurs– Direct Memory Access

(DMA)– Under control of I/O controller

I / O controller signals completion– Interrupt processor– OS resumes suspended

process


12

diskDiskdiskDisk

Memory-I/O busMemory-I/O bus

ProcessorProcessor

CacheCache

MemoryMemoryI/O

controller

I/Ocontroller

Reg

(2) DMA Transfer

(1) Initiate Block Read

(3) Read Done

Motivation #2: Memory management

Multiple processes can reside in physical memory.

How do we resolve address conflicts?– what if two processes access something at the same

address?


13

kernel virtual memory

Memory mapped region forshared libraries

runtime heap (via malloc)

program text (.text)

initialized data (.data)

uninitialized data (.bss)

stack

forbidden0

%esp

memory invisible to user code

the “brk” ptr

Linux/x86 process

memory

image

Solution: Separate virtual addr. spaces

Virtual and physical address spaces divided into equal-sized blocks– blocks are called “pages” (both virtual and physical)

Each process has its own virtual address space– operating system controls how virtual pages as assigned to

physical memory


14

Virtual Address Space for Process 1:

Physical Address Space (DRAM)

VP 1VP 2

PP 2

Address Translation0

0

N-1

0

N-1M-1

VP 1VP 2

PP 7

PP 10

(e.g., read/only library code)

...

...

Virtual Address Space for Process 2:

Motivation #3: Protection

Page table entry contains access rights information– hardware enforces this protection (trap into OS if violation

occurs)


15

Page Tables

Process i:

Physical AddrRead? Write?

PP 9Yes No

PP 4Yes Yes

XXXXXXX No No

VP 0:

VP 1:

VP 2:•••

•••

•••

Process j:

0:1:

N-1:

Memory

Physical AddrRead? Write?

PP 6Yes Yes

PP 9Yes No

XXXXXXX No No•••

•••

•••

VP 0:

VP 1:

VP 2:


16

VM address translation

Virtual Address Space– V = {0, 1, …, N–1}

Physical Address Space– P = {0, 1, …, M–1}– M < N

Address Translation– MAP: V P U {}– For virtual address a:

• MAP(a) = a’ if data at virtual address a is at physical address a’ in P

• MAP(a) = if data at virtual address a is not in physical memory– Either invalid or stored on disk


17

Processor

HardwareAddr TransMechanism

MainMemorya

a'

physical addressvirtual address part of the on-chipmemory mgmt unit (MMU)

VM address translation: Miss

VM address translation: Miss


18

Processor

HardwareAddr TransMechanism

faulthandler

MainMemory

Secondary memorya

a'

page fault

physical addressOS performsthis transfer(only if miss)

virtual address part of the on-chipmemory mgmt unit (MMU)

VM address translation

Parameters– P = 2p = page size (bytes). – N = 2n = Virtual address limit– M = 2m = Physical address limit


19

virtual page number page offset virtual address

physical page number page offset physical address0p–1

address translation

pm–1

n–1 0p–1p

Page offset bits don’t change as a result of translation

Page tables


20

Memory residentpage table

(physical page or disk address) Physical Memory

Disk Storage(swap file orregular file system file)

Valid

1

1

111

1

10

0

0

Virtual PageNumber

Address translation via page table


21

virtual page number (VPN) page offset

virtual address

physical page number (PPN) page offset

physical address

0p–1pm–1

n–1 0p–1ppage table base register

if valid=0then pagenot in memory

valid physical page number (PPN)access

VPN acts astable index

Page table operation

Translation– Separate (set of) page table(s) per process– VPN forms index into page table (points to a page table entry)


22


virtual address


physical address

0p–1pm–1






virtual address


physical address

0p–1pm–1






Computing physical address– Page Table Entry (PTE) provides info about page

• if (valid bit = 1) then the page is in memory.– Use physical page number (PPN) to construct address

• if (valid bit = 0) then the page is on disk - page fault


23


virtual address


physical address

0p–1pm–1






virtual address


physical address

0p–1pm–1






Checking protection– Access rights field indicate allowable access

• e.g., read-only, read-write, execute-only

• typically support multiple protection modes

– Protection violation fault if user doesn’t have necessary permission


24


virtual address


physical address

0p–1pm–1






virtual address


physical address

0p–1pm–1





Multi-level page tables

Given:– 4KB (212) page size– 32-bit address space– 4-byte PTE

Problem:– Would need a 4 MB page table!

• 220 *4 bytes

Common solution– multi-level page tables– e.g., 2-level table (P6)

• Level 1 table: 1024 entries, each of which points to a Level 2 page table.

• Level 2 table: 1024 entries, each of which points to a page


25

Level 1

Table

...

Level 2

Tables

Integrating VM and cache

Most caches “Physically Addressed”– Accessed by physical addresses– Allows multiple processes to have blocks in cache at a time– Allows multiple processes to share pages– Cache doesn’t need to be concerned with protection issues

• Access rights checked as part of address translation

Perform address translation before cache lookup– But this could involve a memory access itself (of the PTE)– Of course, page table entries can also become cached


26

CPUTrans-lation

Cache MainMemory

VA PA miss

hitdata

Speeding up translation with a TLB

• “Translation Lookaside Buffer” (TLB)– Small hardware cache in MMU– Maps virtual page numbers to physical page numbers– Contains complete page table entries for small number of

pages


27

CPUTLB

LookupCache Main

Memory

VA PA miss

hit

data

Trans-lation

hit

miss

Address translation with a TLB


28

virtual addressvirtual page number page offset

physical address

n–1 0p–1p

valid physical page numbertag

valid tag data

data=

cache hit

tag byte offsetindex

=

TLB hit

TLB

Cache

. ..

Taken stock – main themes

Programmer’s view– Large “flat” address space

• Can allocate large blocks of contiguous addresses

– Processor “owns” machine• Has private address space

• Unaffected by behavior of other processes

System view– Virtual address space created by mapping to set of pages

• Need not be contiguous

• Allocated dynamically

• Enforce protection during address translation

– OS manages many processes simultaneously• Continually switching among processes

• Especially when one must wait for resource– E.g., disk I/O to handle page fault


29

Simple memory system

Memory is byte addressable

Access are to 1-byte words

14-bit virtual addresses, 12-bit physical address

• Page size = 64 bytes (26)


30

13 12 11 10 9 8 7 6 5 4 3 2 1 0

11 10 9 8 7 6 5 4 3 2 1 0

VPO

PPOPPN

VPN

(Virtual Page Number) (Virtual Page Offset)

(Physical Page Number) (Physical Page Offset)

Simple memory system page table

Only show first 16 entries


31

VPN PPN Valid

VPN PPN Valid

00 28 1 08 13 1

01 – 0 09 17 1

02 33 1 0A 09 1

03 02 1 0B – 0

04 – 0 0C – 0

05 16 1 0D 2D 1

06 – 0 0E 11 1

07 – 0 0F 0D 1

Simple memory system TLB

TLB– 16 entries– 4-way associative


32

13 12 11 10 9 8 7 6 5 4 3 2 1 0

VPOVPN

TLBITLBT

Set Tag PPN Valid Tag PPN Valid Tag PPN Valid Tag PPN Valid

0 03 – 0 09 0D 1 00 – 0 07 02 1

1 03 2D 1 02 – 0 04 – 0 0A – 0

2 02 – 0 08 – 0 06 – 0 03 – 0

3 07 – 0 03 0D 1 0A 34 1 02 – 0

Simple memory system cache

Cache– 16 lines– 4-byte line size– Direct mapped


33

11 10 9 8 7 6 5 4 3 2 1 0

PPOPPN

COCICT

Idx Tag Valid B0 B1 B2 B3 Idx Tag Valid B0 B1 B2 B3

0 19 1 99 11 23 11 8 24 1 3A 00 51 89

1 15 0 – – – – 9 2D 0 – – – –

2 1B 1 00 02 04 08 A 2D 1 93 15 DA 3B

3 36 0 – – – – B 0B 0 – – – –

4 32 1 43 6D 8F 09 C 12 0 – – – –

5 0D 1 36 72 F0 1D D 16 1 04 96 34 15

6 31 0 – – – – E 13 1 83 77 1B D3

7 16 1 11 C2 DF 03 F 14 0 – – – –


34

Address translation problem 10.12• Virtual address 0x03a9

VPN ___ TLBI ___ TLBTag ____ TLB Hit? __ Page Fault? __ PPN: ____

Physical address

Offset ___ CI___ CT ____ Hit? __ Byte returned: ____

13 12 11 10 9 8 7 6 5 4 3 2 1 0

VPOVPN

TLBITLBT

11 10 9 8 7 6 5 4 3 2 1 0

PPOPPN

COCICT


35

Address translation problem 10.13• Virtual address 0x0040

VPN ___ TLBI ___ TLBTag ____ TLB Hit? __ Page Fault? __ PPN: ____

Physical address

Offset ___ CI___ CT ____ Hit? __ Byte returned: ____

13 12 11 10 9 8 7 6 5 4 3 2 1 0

VPOVPN

TLBITLBT

11 10 9 8 7 6 5 4 3 2 1 0

PPOPPN

COCICT

Harsh reality

Memory matters

Memory is not unbounded– It must be allocated and managed– Many applications are memory dominated

• Especially those based on complex, graph algorithms

Memory referencing bugs especially pernicious– Effects are distant in both time and space

Memory performance is not uniform– Cache and virtual memory effects can greatly affect program

performance– Adapting program to characteristics of memory system can

lead to major speed improvements

Dynamic memory allocation

Explicit vs. implicit memory allocator– Explicit: application allocates and frees space

• E.g., malloc and free in C

– Implicit: application allocates, but does not free space• E.g. garbage collection in Java, ML or Lisp

Allocation– In both cases the memory allocator provides an abstraction of

memory as a set of blocks– Doles out free memory blocks to application

Will discuss simple explicit memory allocation today

Application

Dynamic Memory Allocator

Heap Memory

Process memory image

kernel virtual memory

Memory mapped region forshared libraries

run-time heap (via malloc)

program text (.text)

initialized data (.data)

uninitialized data (.bss)

stack

0

%esp

memory invisible to user code

the “brk” ptr

Allocators requestadditional heap memoryfrom the operating system using the sbrk function.

Malloc package

#include <stdlib.h>void *malloc(size_t size)– If successful:

• Returns a pointer to a memory block of at least size bytes, (typically) aligned to 8-byte boundary.

• If size == 0, returns NULL

– If unsuccessful: returns NULL (0) and sets errno.

void *realloc(void *p, size_t size) – Changes size of block p and returns pointer to new block.– Contents of new block unchanged up to min of old and new

size.

void free(void *p)– Returns the block pointed at by p to pool of available memory– p must come from a previous call to malloc or realloc.

Malloc example

void foo(int n, int m) { int i, *p; /* allocate a block of n ints */ if ((p = (int *) malloc(n * sizeof(int))) == NULL) { perror("malloc"); exit(0); } for (i=0; i<n; i++) p[i] = i;

/* add m bytes to end of p block */ if ((p = (int *) realloc(p, (n+m) * sizeof(int))) == NULL) { perror("realloc"); exit(0); } for (i=n; i < n+m; i++) p[i] = i;

/* print new array */ for (i=0; i<n+m; i++) printf("%d\n", p[i]);

free(p); /* return p to available memory pool */}

Allocation examples

p1 = malloc(4)

p2 = malloc(5)

p3 = malloc(6)

free(p2)

p4 = malloc(2)

Constraints

Applications:– Can issue arbitrary sequence of allocation and free requests– Free requests must correspond to an allocated block

Allocators– Can’t control number or size of allocated blocks– Must respond immediately to all allocation requests

• i.e., can’t reorder or buffer requests

– Must allocate blocks from free memory• i.e., can only place allocated blocks in free memory

– Must align blocks so they satisfy all alignment requirements• 8 byte alignment for GNU malloc (libc malloc) on Linux boxes

– Can only manipulate and modify free memory– Can’t move the allocated blocks once they are allocated

• i.e., compaction is not allowed

Goals of good malloc/free

Primary goals– Good time performance for malloc and free

• Ideally should take constant time (not always possible)

• Should certainly not take linear time in the number of blocks

– Good space utilization• User allocated structures should be large fraction of the heap.

• Want to minimize “fragmentation”.

Some other goals– Good locality properties

• Structures allocated close in time should be close in space

• “Similar” objects should be allocated close in space

– Robust• Can check that free(p1) is on a valid allocated object p1• Can check that memory references are to allocated space

Performance goals: throughput

Given some sequence of malloc and free requests:– R0, R1, ..., Rk, ... , Rn-1

Want to maximize throughput and peak memory utilization.– These goals are often conflicting

Throughput:– Number of completed requests per unit time– Example:

• 5,000 malloc calls and 5,000 free calls in 10 seconds

• Throughput is 10,000 operations/second.

Performance goals: Peak mem utilization

Given some sequence of malloc and free requests:– R0, R1, ..., Rk, ... , Rn-1

Def: Aggregate payload Pk: malloc(p) results in a block with a payload of p bytes.. After request Rk has completed, the aggregate payload Pk is the sum of currently allocated payloads.

Def: Current heap size is denoted by HkAssume that Hk is monotonically nondecreasing

Def: Peak memory utilization: – After k requests, peak memory utilization is:

• Uk = ( maxi<k Pi ) / Hk

Internal fragmentation

• Poor memory utilization caused by fragmentation.– Comes in two forms: internal and external fragmentation

Internal fragmentation– For some block, internal fragmentation is the difference

between the block size and the payload size.

– Caused by overhead of maintaining heap data structures, padding for alignment purposes, or explicit policy decisions (e.g., not to split the block).

– Depends only on the pattern of previous requests, and thus is easy to measure.

payloadInternal fragmentation

block

Internal fragmentation

External fragmentation

p1 = malloc(4)

p2 = malloc(5)

p3 = malloc(6)

free(p2)

p4 = malloc(6)oops!

Occurs when there is enough aggregate heap memory, but no singlefree block is large enough

External fragmentation depends on the pattern of future requests, andthus is difficult to measure.

Implementation issues

How do we know how much memory to free just given a pointer?

How do we keep track of the free blocks?

What do we do with the extra space when allocating a structure that is smaller than the free block it is placed in?

How do we pick a block to use for allocation -- many might fit?

How do we reinsert freed block?

Knowing how much to free

Standard method– Keep the length of a block in the word preceding the

block.• This word is often called the header field or header

– Requires an extra word for every allocated block

free(p0)

p0 = malloc(4) p0

Block size data

5

Keeping track of free blocks

• Method 1: Implicit list using lengths -- links all blocks

• Method 2: Explicit list among the free blocks using pointers within the free blocks

• Method 3: Segregated free list- Different free lists for different size classes

• Method 4: Blocks sorted by size– Can use a balanced tree (e.g. Red-Black tree) with

pointers within each free block, and the length used as a key

5 4 26

5 4 26

Method 1: Implicit List

Need to identify whether each block is free or allocated– Can use extra bit– Bit can be put in the same word as the size if block

sizes are always multiples of two (mask out low order bit when reading size).

size

1 word

Format ofallocated andfree blocks

payload

a = 1: allocated block a = 0: free block

size: block size

payload: application data(allocated blocks only)

a

optionalpadding

Implicit list: Finding a free block

First fit:– Search list from beginning, choose first free block that

fits– Can take linear time in total number of blocks (allocated

and free)– In practice it can cause “splinters” at beginning of list

Next fit:– Like first-fit, but search list from location of end of

previous search– Research suggests that fragmentation is worse

Best fit:– Search the list, choose the free block with the closest

size that fits– Keeps fragments small --- usually helps fragmentation– Will typically run slower than first-fit

Implicit list: Allocating in free block

• Allocating in a free block - splitting– Since allocated space might be smaller than free

space, we might want to split the block

void addblock(ptr p, int len) { int newsize = ((len + 1) >> 1) << 1; // add 1 and round up int oldsize = *p & -2; // mask out low bit *p = newsize | 1; // set new length if (newsize < oldsize) *(p+newsize) = oldsize - newsize; // set length in remaining} // part of block

4 4 26

4 24

p

24

addblock(p, 2)

Implicit list: Freeing a block

Simplest implementation:– Only need to clear allocated flag– But can lead to “false fragmentation”

There is enough free space, but the allocator won’t be able to find it

4 24 2

free(p) p

4 4 2

4

4 2

malloc(5)Oops!

Implicit list: Coalescing

• Join (coelesce) with next and/or previous block if they are free– Coalescing with next block

– But how do we coalesce with previous block?

4 24 2

free(p) p

4 4 2

4

6

void free_block(ptr p) { *p = *p & -2; // clear allocated flag next = p + *p; // find next block if ((*next & 1) == 0) *p = *p + *next; // add to this block if} // not allocated

Implicit list: Bidirectional coalescing

• Boundary tags [Knuth73]– Replicate size/allocated word at bottom of free blocks– Allows us to traverse the “list” backwards, but requires extra

space– Important and general technique!

size

1 word

Format ofallocated andfree blocks

payload andpadding

a = 1: allocated block a = 0: free block

size: total block size

payload: application data(allocated blocks only)

a

size aBoundary tag (footer)

4 4 4 4 6 46 4

Header

Constant time coalescing

allocated

allocated

allocated

free

free

allocated

free

free

block beingfreed

Case 1 Case 2 Case 3 Case 4

m1 1

Constant time coalescing (Case 1)

m1 1

n 1

n 1

m2 1

m2 1

m1 1

m1 1

n 0

n 0

m2 1

m2 1

m1 1


m1 1

n+m2 0

n+m2 0

m1 1

m1 1

n 1

n 1

m2 0

m2 0

m1 0


m1 0

n 1

n 1

m2 1

m2 1

n+m1 0

n+m1 0

m2 1

m2 1

m1 0


m1 0

n 1

n 1

m2 0

m2 0

n+m1+m2 0

n+m1+m2 0

Summary of key allocator policies

Placement policy:– First fit, next fit, best fit, etc.– Trades off lower throughput for less fragmentation

Splitting policy:– When do we go ahead and split free blocks?– How much internal fragmentation are we willing to tolerate?

Coalescing policy:– Immediate coalescing: coalesce adjacent blocks each time

free is called – Deferred coalescing: try to improve performance of free by

deferring coalescing until needed. e.g.,• Coalesce as you scan the free list for malloc.

• Coalesce when the amount of external fragmentation reaches some threshold.

Implicit lists: summary

Implementation: very simple

Allocate: linear time worst case

Free: constant time worst case -- even with coalescing

Memory usage: will depend on placement policy– First fit, next fit or best fit

Not used in practice for malloc/free because of linear time allocate. Used in many special purpose applications.

However, the concepts of splitting and boundary tag coalescing are general to all allocators.

Implicit mem. mgmnt: Garbage collection• Garbage collection: automatic reclamation of heap-

allocated storage -- application never has to free

Common in functional languages, scripting languages, and modern object oriented languages:– Lisp, ML, Java, Perl, Mathematica,

Variants (conservative garbage collectors) exist for C and C++– Cannot collect all garbage

void foo() { int *p = malloc(128); return; /* p block is now garbage */}

Garbage collection

How does the memory manager know when memory can be freed?– In general we cannot know what is going to be used

in the future since it depends on conditionals– But we can tell that certain blocks cannot be used if

there are no pointers to them

Need to make certain assumptions about pointers– Memory manager can distinguish pointers from

non-pointers– All pointers point to the start of a block

Memory as a graph

We view memory as a directed graph– Each block is a node in the graph – Each pointer is an edge in the graph– Locations not in the heap that contain pointers into the heap

are called root nodes (e.g. registers, locations on the stack, global variables)

Root nodes

Heap nodes

Not-reachable(garbage)

reachable

A node (block) is reachable if there is a path from any root to that node. Non-reachable nodes are garbage (never needed by the application)

Mark and sweep collecting

Can build on top of malloc/free package– Allocate using malloc until you “run out of space”

When out of space:– Use extra mark bit in the head of each block– Mark: Start at roots and set mark bit on all reachable

memory– Sweep: Scan all blocks and free blocks that are not marked

Before mark

root

After mark

After sweep free

Mark bit set

free

Memory-related bugs

Dereferencing bad pointers

Reading uninitialized memory

Overwriting memory

Referencing nonexistent variables

Freeing blocks multiple times

Referencing freed blocks

Failing to free blocks

Dereferencing bad pointers

• The classic scanf bug

scanf(“%d”, val);

Reading uninitialized memory

Assuming that heap data is initialized to zero

/* return y = Ax */int *matvec(int **A, int *x) { int *y = malloc(N*sizeof(int)); int i, j;

for (i=0; i<N; i++) for (j=0; j<N; j++) y[i] += A[i][j]*x[j]; return y;}

Overwriting memory

Allocating the (possibly) wrong sized object

int **p;

p = malloc(N*sizeof(int));

for (i=0; i<N; i++) { p[i] = malloc(M*sizeof(int));}

Overwriting memory

Off-by-one error

int **p;

p = malloc(N*sizeof(int *));

for (i=0; i<=N; i++) { p[i] = malloc(M*sizeof(int));}

Overwriting memory

Not checking the max string size

Basis for classic buffer overflow attacks– 1988 Internet worm– Modern attacks on Web servers

char s[8];int i;

gets(s); /* reads “123456789” from stdin */

Overwriting memory

Referencing a pointer instead of the object it points to

int *BinheapDelete(int **binheap, int *size) { int *packet; packet = binheap[0]; binheap[0] = binheap[*size - 1]; *size--; Heapify(binheap, *size, 0); return(packet);}

Overwriting memory

Misunderstanding pointer arithmetic

int *search(int *p, int val) { while (*p && *p != val) p += sizeof(int);

return p;}

Referencing nonexistent variables

Forgetting that local variables disappear when a function returns

int *foo () { int val; return &val;}

Freeing blocks multiple times

Nasty!

x = malloc(N*sizeof(int));<manipulate x>free(x);

y = malloc(M*sizeof(int));<manipulate y>free(x);

Referencing freed blocks

Evil!

x = malloc(N*sizeof(int));<manipulate x>free(x);...y = malloc(M*sizeof(int));for (i=0; i<M; i++) y[i] = x[i]++;

Failing to free blocks (memory leaks)

Slow, long-term killer!

foo() { int *x = malloc(N*sizeof(int)); ... return;}

Fabián E. Bustamante, Spring 2007 Virtual Memory Today Motivations for VM Address translation Accelerating translation with TLBs Dynamic memory allocation.

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