Dynamic Memory Management
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1 Dynamic Memory Management 1
2 Dynamic Memory Management I) Why care 2
3 Dynamic Memory Management I) Why care II) Requirements 2
4 Dynamic Memory Management I) Why care II) Requirements III) Basic Techniques 2
5 Dynamic Memory Management I) Why care II) Requirements III) Basic Techniques IV) False Sharing 2
6 Dynamic Memory Management I) Why care II) Requirements III) Basic Techniques IV) False Sharing V) RMalloc 2
7 Dynamic Memory Management I) Why care II) Requirements III) Basic Techniques IV) False Sharing V) RMalloc VI) Benchmarks 2
8 Motivation Why do we have to care about memory management? 3
9 Motivation Why do we have to care about memory management? Answer: LibBEM, building DLP-PC operator, n = SUN PTmalloc rmalloc s (1930 MB) s (1835 MB) s (1894 MB) 4
10 Motivation Why do we have to care about memory management? Answer: LibBEM, building DLP-PC operator, n = SUN PTmalloc rmalloc s (1930 MB) s (1835 MB) s (1894 MB) H-matrix Multiplication, constant rank p SUN PTmalloc rmalloc s (82 MB) 98.4 s (81 MB) 99.5 s (84 MB) s (100 MB) 7.3 s (94 MB) 7.2 s (109 MB) 5
11 Requirements 6
12 Requirements O (1) complexity for malloc and free 7
13 Requirements O (1) complexity for malloc and free fast handling of small objects 7
14 Requirements O (1) complexity for malloc and free fast handling of small objects Parallel efficiency: no blocking when running in parallel 7
15 Requirements O (1) complexity for malloc and free fast handling of small objects Parallel efficiency: no blocking when running in parallel Parallel safety: no data corruption when called simultaneously 7
16 Requirements O (1) complexity for malloc and free fast handling of small objects Parallel efficiency: no blocking when running in parallel Parallel safety: no data corruption when called simultaneously should handle large memory footprint (> 64GB) 7
17 Requirements O (1) complexity for malloc and free fast handling of small objects Parallel efficiency: no blocking when running in parallel Parallel safety: no data corruption when called simultaneously should handle large memory footprint (> 64GB) minimal memory allocation, low fragmentation 7
18 Fragmentation 8
19 Fragmentation External fragmentation: memory allocator has free chunks of memory but can t serve request usual cause: available blocks are too small Memory Request 9
20 Fragmentation External fragmentation: memory allocator has free chunks of memory but can t serve request usual cause: available blocks are too small Memory Request Internal fragmentation: returned block is larger than requested: remainder is wasted 10
21 Fragmentation External fragmentation: memory allocator has free chunks of memory but can t serve request usual cause: available blocks are too small Memory Request Internal fragmentation: returned block is larger than requested: remainder is wasted Options: Splitting: split larger block and put remainder back into allocator 11
22 Fragmentation External fragmentation: memory allocator has free chunks of memory but can t serve request usual cause: available blocks are too small Memory Request Internal fragmentation: returned block is larger than requested: remainder is wasted Options: Splitting: split larger block and put remainder back into allocator Coalescing: if free chunks are adjacent in memory, join them 11
23 Basic Techniques Sequential Fits 12
24 Basic Techniques Sequential Fits Single list of free blocks: next size next size next size next size data data data data 13
25 Basic Techniques Sequential Fits Single list of free blocks: next size next size next size next size data data data data Strategies for malloc: First fit: use first chunk large enough to hold data high internal fragmentation without splitting 14
26 Basic Techniques Sequential Fits Single list of free blocks: next size next size next size next size data data data data Strategies for malloc: First fit: use first chunk large enough to hold data high internal fragmentation without splitting Best fit: use smallest chunk large enough to hold data in theory: higher memory usage than first fit 14
27 Basic Techniques Sequential Fits Single list of free blocks: next size next size next size next size data data data data Strategies for malloc: First fit: use first chunk large enough to hold data high internal fragmentation without splitting Best fit: use smallest chunk large enough to hold data in theory: higher memory usage than first fit Properties: slow: using linear search (with trees: logarithmic) 15
28 Basic Techniques Sequential Fits Single list of free blocks: next size next size next size next size data data data data Strategies for malloc: First fit: use first chunk large enough to hold data high internal fragmentation without splitting Best fit: use smallest chunk large enough to hold data in theory: higher memory usage than first fit Properties: slow: using linear search (with trees: logarithmic) bad scaling w.r.t. processors and memory 15
29 Segregated Free Lists 16
30 Segregated Free Lists Define size classes: S 0 < S 1 < < S n, S i 17
31 Segregated Free Lists Define size classes: S 0 < S 1 < < S n, S i A size s belongs to size class S i if S i 1 < s S i 18
32 Segregated Free Lists Define size classes: S 0 < S 1 < < S n, S i A size s belongs to size class S i if S i 1 < s S i Typical definition: by power series S i = b i, b > 1 19
33 Segregated Free Lists: use array of lists for each size class S 6 S 5 S 4 S 3 S 2 S 1 S 0 20
34 Segregated Free Lists: use array of lists for each size class S 6 S 5 S 4 S 3 S 2 S 1 S 0 fast malloc and free: only small lists for searching good fit (almost best fit) strategy: size of unused part is bounded (by b) 21
35 Segregated Free Lists: use array of lists for each size class S 6 S 5 S 4 S 3 S 2 S 1 S 0 fast malloc and free: only small lists for searching good fit (almost best fit) strategy: size of unused part is bounded (by b) Option: round up each size to size of class O(1) complexity for malloc and free (all blocks in class have same size) higher memory consumption, but bounded internal fragmentation 22
36 Buddy Systems 23
37 special case of segregated free lists Buddy Systems 24
38 special case of segregated free lists Buddy Systems memory is hierarchically divided into two chunks (buddies) by fixed rules 24
39 Buddy Systems special case of segregated free lists memory is hierarchically divided into two chunks (buddies) by fixed rules each chunk can only be merged with his buddy 24
40 Buddy Systems special case of segregated free lists memory is hierarchically divided into two chunks (buddies) by fixed rules each chunk can only be merged with his buddy Advantage: very high speed, little memory overhead 24
41 Buddy Systems special case of segregated free lists memory is hierarchically divided into two chunks (buddies) by fixed rules each chunk can only be merged with his buddy Advantage: very high speed, little memory overhead Disadvantage: high internal fragmentation possible due to fixed splitting rules 24
42 Buddy Systems special case of segregated free lists memory is hierarchically divided into two chunks (buddies) by fixed rules each chunk can only be merged with his buddy Advantage: very high speed, little memory overhead Disadvantage: high internal fragmentation possible due to fixed splitting rules Examples: Binary buddies: size classes are power of 2 25
43 Buddy Systems special case of segregated free lists memory is hierarchically divided into two chunks (buddies) by fixed rules each chunk can only be merged with his buddy Advantage: very high speed, little memory overhead Disadvantage: high internal fragmentation possible due to fixed splitting rules Examples: Binary buddies: size classes are power of 2 25
44 Buddy Systems special case of segregated free lists memory is hierarchically divided into two chunks (buddies) by fixed rules each chunk can only be merged with his buddy Advantage: very high speed, little memory overhead Disadvantage: high internal fragmentation possible due to fixed splitting rules Examples: Binary buddies: size classes are power of 2 25
45 Buddy Systems special case of segregated free lists memory is hierarchically divided into two chunks (buddies) by fixed rules each chunk can only be merged with his buddy Advantage: very high speed, little memory overhead Disadvantage: high internal fragmentation possible due to fixed splitting rules Examples: Binary buddies: size classes are power of 2 25
46 Buddy Systems special case of segregated free lists memory is hierarchically divided into two chunks (buddies) by fixed rules each chunk can only be merged with his buddy Advantage: very high speed, little memory overhead Disadvantage: high internal fragmentation possible due to fixed splitting rules Examples: Binary buddies: size classes are power of 2 Fibonacci buddies: uses Fibonacci series for splitting 25
47 False Sharing Problem: access to memory is done via cache (memory hierarchy) 26
48 False Sharing Problem: access to memory is done via cache (memory hierarchy) smallest block in cache is a cacheline (usually 64 bytes long) 26
49 False Sharing Problem: access to memory is done via cache (memory hierarchy) smallest block in cache is a cacheline (usually 64 bytes long) changing one byte in cacheline make whole cacheline invalid 26
50 False Sharing Problem: access to memory is done via cache (memory hierarchy) smallest block in cache is a cacheline (usually 64 bytes long) changing one byte in cacheline make whole cacheline invalid writing data on one processor leads to memory access on other processor Cacheline x=5 y=4 p = i p = j x=8 y=4 data changed complete cacheline invalid 26
51 How to avoid false sharing: align all data-blocks at 64 byte addresses 27
52 How to avoid false sharing: align all data-blocks at 64 byte addresses cacheline can not be shared by different data but: high internal fragmentation 27
53 How to avoid false sharing: align all data-blocks at 64 byte addresses cacheline can not be shared by different data but: high internal fragmentation use private heaps for different processors (PTmalloc, LKmalloc) p=0 p=1 p=2 27
54 How to avoid false sharing: align all data-blocks at 64 byte addresses cacheline can not be shared by different data but: high internal fragmentation use private heaps for different processors (PTmalloc, LKmalloc) p=0 p=1 p=2 data on different processors is allocated from different areas important: freed chunks must be returned to their heaps memory management is completely independent on different processors but: memory consumption grows linearly in p (higher external fragmentation) 27
55 Hoard: avoiding false sharing mostly with a bounded ext. fragmentation private heaps for each processor 28
56 Hoard: avoiding false sharing mostly with a bounded ext. fragmentation private heaps for each processor chunks are collected in superblocks of a fixed size S 0 S 1 thereby avoiding false sharing 28
57 Hoard: avoiding false sharing mostly with a bounded ext. fragmentation private heaps for each processor chunks are collected in superblocks of a fixed size S 0 S 1 thereby avoiding false sharing if free part of a superblock exceeds some threshold, move superblock to a global heap (bounded ext. fragmentation) 28
58 Hoard: avoiding false sharing mostly with a bounded ext. fragmentation private heaps for each processor chunks are collected in superblocks of a fixed size S 0 S 1 thereby avoiding false sharing if free part of a superblock exceeds some threshold, move superblock to a global heap (bounded ext. fragmentation) allocate new superblock by moving superblock from global heap 28
59 Hoard: avoiding false sharing mostly with a bounded ext. fragmentation private heaps for each processor chunks are collected in superblocks of a fixed size S 0 S 1 thereby avoiding false sharing if free part of a superblock exceeds some threshold, move superblock to a global heap (bounded ext. fragmentation) allocate new superblock by moving superblock from global heap blocking only when accessing global heap (good parallel speedup) 28
60 Hoard: avoiding false sharing mostly with a bounded ext. fragmentation private heaps for each processor chunks are collected in superblocks of a fixed size S 0 S 1 thereby avoiding false sharing if free part of a superblock exceeds some threshold, move superblock to a global heap (bounded ext. fragmentation) allocate new superblock by moving superblock from global heap blocking only when accessing global heap (good parallel speedup) but: maximal managed size defined by size of superblock 28
61 RMalloc uses per-processor heap (although O (p) memory consumption) 29
62 RMalloc uses per-processor heap (although O (p) memory consumption) different strategy for different sizes: small, middle and large chunks Large chunks ( 260MB): directly managed by operating system (mmap) 29
63 RMalloc uses per-processor heap (although O (p) memory consumption) different strategy for different sizes: small, middle and large chunks Large chunks ( 260MB): directly managed by operating system (mmap) Middle sized chunks (256B < s < 260MB): segregated free lists with immediate coalescing and splitting 29
64 RMalloc uses per-processor heap (although O (p) memory consumption) different strategy for different sizes: small, middle and large chunks Large chunks ( 260MB): directly managed by operating system (mmap) Middle sized chunks (256B < s < 260MB): segregated free lists with immediate coalescing and splitting Small chunks ( 256B): stored in containers (superblocks) of a fixed size (8kB) unused containers are cached and can be reused by different size classes avoids splitting/coalescing for small blocks, reduces external fragmentation 29
65 heaps are tightly bounded to threads; only if thread finishes, heap can be reused by new thread: no sharing of data possible 30
66 heaps are tightly bounded to threads; only if thread finishes, heap can be reused by new thread: no sharing of data possible memory is preallocated from operating system to reduce no. of system calls 30
67 heaps are tightly bounded to threads; only if thread finishes, heap can be reused by new thread: no sharing of data possible memory is preallocated from operating system to reduce no. of system calls size of preallocated area dependent on current memory usage 30
68 Benchmarks 31
69 Benchmarks H-building, fixed precision malloc memory SUN 1.2 s 5.3 s 24.9 s s 592 MB 32
70 Benchmarks H-building, fixed precision malloc memory SUN 1.2 s 5.3 s 24.9 s s 592 MB PT 1.6 s 6.7 s 30.7 s s 543 MB 32
71 Benchmarks H-building, fixed precision malloc memory SUN 1.2 s 5.3 s 24.9 s s 592 MB PT 1.6 s 6.7 s 30.7 s s 543 MB RM 1.2 s 5.0 s 20.9 s 87.2 s 572 MB 32
72 Benchmarks H-building, fixed precision malloc memory SUN 1.2 s 5.3 s 24.9 s s 592 MB PT 1.6 s 6.7 s 30.7 s s 543 MB RM 1.2 s 5.0 s 20.9 s 87.2 s 572 MB LU-factorisation, fixed precision malloc memory SUN 3.4 s 27.1 s s s 1613 MB 33
73 Benchmarks H-building, fixed precision malloc memory SUN 1.2 s 5.3 s 24.9 s s 592 MB PT 1.6 s 6.7 s 30.7 s s 543 MB RM 1.2 s 5.0 s 20.9 s 87.2 s 572 MB LU-factorisation, fixed precision malloc memory SUN 3.4 s 27.1 s s s 1613 MB PT 3.3 s 27.2 s s s 1562 MB 33
74 Benchmarks H-building, fixed precision malloc memory SUN 1.2 s 5.3 s 24.9 s s 592 MB PT 1.6 s 6.7 s 30.7 s s 543 MB RM 1.2 s 5.0 s 20.9 s 87.2 s 572 MB LU-factorisation, fixed precision malloc memory SUN 3.4 s 27.1 s s s 1613 MB PT 3.3 s 27.2 s s s 1562 MB RM 3.4 s 26.0 s s s 1599 MB 33
75 H-inversion, fixed precision malloc memory SUN 56.7 s s s 1399 MB 34
76 H-inversion, fixed precision malloc memory SUN 56.7 s s s 1399 MB PT 56.3 s s s 1373 MB 34
77 H-inversion, fixed precision malloc memory SUN 56.7 s s s 1399 MB PT 56.3 s s s 1373 MB RM 56.1 s s s 1399 MB 34
78 H-inversion, fixed precision malloc memory SUN 56.7 s s s 1399 MB PT 56.3 s s s 1373 MB RM 56.1 s s s 1399 MB H-matrix Multiplication, fixed rank malloc t(1) t(4) t(8) t(12) t(16) memory SUN 98.6 s 34.9 s 39.4 s 45.6 s 47.3 s 100 MB 35
79 H-inversion, fixed precision malloc memory SUN 56.7 s s s 1399 MB PT 56.3 s s s 1373 MB RM 56.1 s s s 1399 MB H-matrix Multiplication, fixed rank malloc t(1) t(4) t(8) t(12) t(16) memory SUN 98.6 s 34.9 s 39.4 s 45.6 s 47.3 s 100 MB PT 98.4 s 26.8 s 13.7 s 9.3 s 7.3 s 94 MB 35
80 H-inversion, fixed precision malloc memory SUN 56.7 s s s 1399 MB PT 56.3 s s s 1373 MB RM 56.1 s s s 1399 MB H-matrix Multiplication, fixed rank malloc t(1) t(4) t(8) t(12) t(16) memory SUN 98.6 s 34.9 s 39.4 s 45.6 s 47.3 s 100 MB PT 98.4 s 26.8 s 13.7 s 9.3 s 7.3 s 94 MB MT 99.3 s 26.1 s 13.3 s 9.1 s 7.2 s 144 MB 35
81 H-inversion, fixed precision malloc memory SUN 56.7 s s s 1399 MB PT 56.3 s s s 1373 MB RM 56.1 s s s 1399 MB H-matrix Multiplication, fixed rank malloc t(1) t(4) t(8) t(12) t(16) memory SUN 98.6 s 34.9 s 39.4 s 45.6 s 47.3 s 100 MB PT 98.4 s 26.8 s 13.7 s 9.3 s 7.3 s 94 MB MT 99.3 s 26.1 s 13.3 s 9.1 s 7.2 s 144 MB RM 99.5 s 26.2 s 13.3 s 9.0 s 7.2 s 109 MB 35
82 libbem, building PC operator malloc memory SUN 48.6 s s s 8609 MB 36
83 libbem, building PC operator malloc memory SUN 48.6 s s s 8609 MB PT 46.4 s s s 8176 MB 36
84 libbem, building PC operator malloc memory SUN 48.6 s s s 8609 MB PT 46.4 s s s 8176 MB RM 45.6 s s s 8395 MB 36
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