Scala 具体的并行集合库【翻译】

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本文内容

  • 并行数组(Parallel Array)
  • 并行向量(Parallel Vector)
  • 并行范围(Parallel Range)
  • 并行哈希表(Parallel Hash Tables)
  • 并行散列 Tries(Parallel Hash Tries)
  • 并行并发 Tries(Parallel Concurrent Tries)
  • 参考资料

并行数组(Parallel Array)


一个 ParArray 序列包含线性、连续的元素数组。这意味着,通过修改底层数组,可以高效地访问和修改元素。因此,反序元素也很高效。并行数组跟数组一样也是固定大小的。

scala> val pa = scala.collection.parallel.mutable.ParArray.tabulate(1000)(x =>2*

 x +1)

pa: scala.collection.parallel.mutable.ParArray[Int] = ParArray(1, 3, 5, 7, 9, 11

, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51

, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91

, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 12

5, 127, 129, 131, 133, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155, 15

7, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 179, 181, 183, 185, 187, 18

9, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 22

1, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 25

3, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 28

5, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315,...

 

scala> pa reduce(_+_)

res0: Int = 1000000

 

scala> pa map(x=>(x-1)/2)

res1: scala.collection.parallel.mutable.ParArray[Int] = ParArray(0, 1, 2, 3, 4,

5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 2

6, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 4

6, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 6

6, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 8

6, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,

105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,

121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,

137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152,

153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 16...

 

scala>

并行向量(Parallel Vector)


一个 ParVector 是一个不可变序列,具有低常量因子对数的访问(low-constant factor logarithmic access )和更新时间。

scala> val pv = scala.collection.parallel.immutable.ParVector.tabulate(1000)(x =

> x)

pv: scala.collection.parallel.immutable.ParVector[Int] = ParVector(0, 1, 2, 3, 4

, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,

 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,

 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,

 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85,

 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104

, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120

, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136

, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152

, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, ...

 

scala> pv filter (_ %2==0)

res2: scala.collection.parallel.immutable.ParVector[Int] = ParVector(0, 2, 4, 6,

 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46,

48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86,

88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 1

22, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 1

54, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 1

86, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 2

18, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 2

50, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 2

82, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312...

 

scala>

并行范围(Parallel Range)


一个 ParRange 是一个有序的等差整数数列(an ordered sequence of elements equally spaced apart)。并行范围(parallel range)的创建与顺序范围类似(sequential Range)。

scala> 1 to 3 par

warning: there were 1 feature warning(s); re-run with -feature for details

res3: scala.collection.parallel.immutable.ParRange = ParRange(1, 2, 3)

 

scala> 15 to 5 by -2 par

warning: there were 1 feature warning(s); re-run with -feature for details

res4: scala.collection.parallel.immutable.ParRange = ParRange(15, 13, 11, 9, 7,

5)

 

scala>

并行哈希表(Parallel Hash Tables)


并行哈希表(Parallel hash tables)存储底层数组的元素,并将它们放置在由各自元素哈希码的位置。并行可变哈希集(mutable.ParHashSet)和并行可变哈希映射(mutable.ParHashMap)都是基于哈希表。

scala> val phs = scala.collection.parallel.mutable.ParHashSet(1 until 2000: _*)

phs: scala.collection.parallel.mutable.ParHashSet[Int] = ParHashSet(307, 1705, 3

67, 1007, 1954, 1067, 1316, 1033, 707, 1980, 335, 1093, 395, 1342, 1016, 644, 12

65, 35, 704, 95, 1042, 344, 1291, 1044, 404, 1991, 1351, 653, 1353, 1602, 1662,

1379, 1053, 41, 1628, 1302, 1688, 1362, 1079, 381, 1328, 441, 1388, 690, 1637, 9

39, 1390, 1697, 52, 999, 18, 1699, 639, 78, 1665, 1339, 327, 1, 1725, 1399, 1648

, 1365, 27, 1708, 1425, 727, 1674, 976, 1734, 89, 1036, 1983, 676, 115, 736, 38,

 985, 287, 1045, 1685, 347, 64, 1745, 313, 1711, 1385, 373, 1013, 1771, 1073, 13

22, 1382, 773, 75, 1022, 1969, 324, 1082, 384, 1657, 1331, 633, 350, 24, 693, 41

0, 84, 1357, 659, 333, 50, 1731, 719, 393, 110, 359, 1059, 419, 361, 668, 1119,

421, 1368, 728, 670, 1617, 730, 1677, 1394, 979, 696, 370, 1643, 1317, 756, 4...

 

scala> phs map (x => x * x)

res5: scala.collection.parallel.mutable.ParHashSet[Int] = ParHashSet(11236, 2563

201, 1957201, 143641, 227529, 3556996, 1214404, 1946025, 670761, 2181529, 219024

, 1648656, 2062096, 2152089, 343396, 2418025, 1582564, 440896, 925444, 312481, 3

526884, 2775556, 3359889, 175561, 35721, 84681, 2244004, 20164, 2102500, 576081,

 1557504, 338724, 952576, 300304, 1030225, 3139984, 687241, 1227664, 2627641, 35

75881, 51984, 851929, 94249, 2505889, 3972049, 788544, 1459264, 2937796, 2920681

, 2446096, 413449, 59536, 690561, 306916, 441, 2647129, 5929, 1054729, 746496, 3

392964, 3207681, 2989441, 2547216, 180625, 1868689, 166464, 33124, 237169, 25856

64, 1, 3625216, 57600, 99225, 315844, 251001, 238144, 32761, 595984, 1194649, 12

18816, 676, 1464100, 797449, 2131600, 1527696, 2383936, 786769, 3697929, 3222...

 

scala>

并行散列 Tries(Parallel Hash Tries)


并行哈希 tries(Parallel hash tries )是不可变哈希 tries(immutable hash tries)的并行版本,它用来高效地表示不可变集(immutable sets)和映射(immutable maps)。他们由 immutable.ParHashSet 和 immutable.ParHashMap 支持。

scala> val phs = scala.collection.parallel.immutable.ParHashSet(1 until 1000: _

)

phs: scala.collection.parallel.immutable.ParHashSet[Int] = ParSet(645, 892, 69,

809, 629, 365, 138, 760, 101, 479, 347, 846, 909, 333, 628, 249, 893, 518, 962,

468, 234, 941, 777, 555, 666, 88, 481, 352, 408, 977, 170, 523, 582, 762, 115,

83, 730, 217, 276, 994, 308, 741, 5, 873, 449, 120, 247, 379, 878, 440, 655, 51

, 614, 269, 677, 202, 597, 861, 10, 385, 384, 56, 533, 550, 142, 500, 797, 715,

472, 814, 698, 747, 913, 945, 340, 538, 153, 930, 670, 829, 174, 404, 898, 185,

42, 782, 709, 841, 417, 24, 973, 885, 288, 301, 320, 565, 436, 37, 25, 651, 257

 389, 52, 724, 14, 570, 184, 719, 785, 372, 504, 110, 587, 619, 838, 917, 702,

51, 802, 125, 344, 934, 357, 196, 949, 542, 460, 157, 817, 902, 559, 638, 853,

89, 20, 421, 870, 46, 969, 93, 606, 284, 770, 881, 416, 325, 152, 228, 289, 4..

 

scala> phs map { x => x * x } sum

warning: there were 1 feature warning(s); re-run with -feature for details

res6: Int = 332833500

 

scala>

并行并发 tries(Parallel Concurrent Tries)


concurrent.TrieMap 是一个并发线程安全的映射(map),而mutable.ParTrieMap 是它的并行版本。若果数据结构在遍历期间被修改,那么大多数并发数据结构不能保证一致性,Ctries 保证在下一次迭代中更新是可见的。这意味着,当你遍历是,可以改变并发 trie,如下例子所示,输出1到99的平方根。

scala> val numbers = scala.collection.parallel.mutable.ParTrieMap((1 until 100)

zip (1 until 100): _*) map {case(k, v)=>(k.toDouble, v.toDouble)}

numbers: scala.collection.parallel.mutable.ParTrieMap[Double,Double] = ParTrieMa

p(15.0 -> 15.0, 51.0 -> 51.0, 33.0 -> 33.0, 48.0 -> 48.0, 84.0 -> 84.0, 30.0 ->

30.0, 66.0 -> 66.0, 12.0 -> 12.0, 27.0 -> 27.0, 9.0 -> 9.0, 99.0 -> 99.0, 81.0 -

> 81.0, 63.0 -> 63.0, 45.0 -> 45.0, 78.0 -> 78.0, 60.0 -> 60.0, 96.0 -> 96.0, 19

.0 -> 19.0, 1.0 -> 1.0, 37.0 -> 37.0, 52.0 -> 52.0, 88.0 -> 88.0, 34.0 -> 34.0,

70.0 -> 70.0, 16.0 -> 16.0, 67.0 -> 67.0, 13.0 -> 13.0, 49.0 -> 49.0, 85.0 -> 85

.0, 31.0 -> 31.0, 82.0 -> 82.0, 64.0 -> 64.0, 46.0 -> 46.0, 5.0 -> 5.0, 56.0 ->

56.0, 2.0 -> 2.0, 38.0 -> 38.0, 74.0 -> 74.0, 20.0 -> 20.0, 89.0 -> 89.0, 71.0 -

> 71.0, 17.0 -> 17.0, 53.0 -> 53.0, 35.0 -> 35.0, 86.0 -> 86.0, 68.0 -> 68.0, 32

.0 -> 32.0, 50.0 -> 50.0, 83.0 -> 83.0, 6.0 -> 6.0, 42.0 -> 42.0, 24.0 -> 24....

 

scala> while(numbers.nonEmpty){

 | numbers foreach{case(num, sqrt)=>

 | val nsqrt =0.5*(sqrt + num / sqrt)

 | numbers(num)= nsqrt

 | if(math.abs(nsqrt - sqrt)<0.01){

 | println(num, nsqrt)

 | numbers.remove(num)

 | }

 | }

 | }

(1.0,1.0)

(2.0,1.4142156862745097)

(5.0,2.2360688956433634)

(6.0,2.4494943716069653)

(3.0,1.7320508100147274)

(7.0,2.64576704419029)

(4.0,2.0000000929222947)

(15.0,3.872983698008724)

(12.0,3.4641016533502986)

(9.0,3.000000001396984)

(19.0,4.358901750853372)

(16.0,4.000000636692939)

(13.0,3.6055513629176015)

(20.0,4.4721402170657)

……

 

scala>

性能特征


顺序类型(sequence types)的性能特点:

 

head

tail

apply

update

prepend

append

insert

ParArray

C

L

C

C

L

L

L

ParVector

eC

eC

eC

eC

eC

eC

-

ParRange

C

C

C

-

-

-

-

集(set)和映射(map)类型的性能特点:

 

lookup

add

remove

immutable

 

 

 

ParHashSet/ParHashMap

eC

eC

eC

mutable

 

 

 

ParHashSet/ParHashMap

C

C

C

ParTieMap

eC

eC

eC

键(Key)

上面两个表的条目,说明如下:

该操作花费常量时间(快)

eC

该操作有效地花费常量时间,但可能依赖于某些假设,如向量的最大长度或哈希键的离散性

aC

该操作花费分期常量时间。Some invocations of the operation might take longer, but if many operations are performed on average only constant time per operation is taken.

Log

该操作花费时间与集合大小的对数成比例

L

该操作是线性的,花费的时间与集合大小成比例

-

该操作不被支持

下表处理序列类型——可变和不可变——具备如下操作:

head

选择序列的第一个元素

tail

产生一个由除了第一个元素的所有元素组成的新序列

apply

索引

update

对于不可变序列(immutable sequence)的函数式更新,对于可变序列(mutable sequences)的副作用(side effect)更新

prepend

添加一个元素到序列前面。针对不可变序列,这将产生一个新序列。针对可变序列,这将修改已经存在的序列

append

添加一个元素到序列尾部。针对不可变的序列,这将产生一个新序列,针对可变序列,这将修改已经存在的序列。

insert

在序列中的任意位置插入一个元素。只支持可变序列(mutable sequence)

下表处理可变和不可变集(set)和映射(map)具有如下操作:

lookup

测试一个元素是否包含在集(set)中,或选择与键有关的值

add

添加一个元素到集(set),或添加键/值对到映射(map)

remove

从集(set)或删除一个元素,或从映射(map)删除一个键

min

集(set)中最小的元素,或映射(map)中最小的键

参考资料


  • Scala 官网

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