1. Janus: exploiting parallelism via hindsight
Roman Manevich
Omer Tripp
John Field
Mooly Sagiv

2. the PMD code analyzer

3. popular open-source code analyzer (~2,000 weekly downloads)
the PMD code analyzer
popular open-source code analyzer (~2,000 weekly downloads)

4. the PMD code analyzer
popular open-source code analyzer (~2,000 weekly downloads)
scans source files modularly => parallelization opportunity

5. the main loop of PMD (simplified)

6. the main loop of PMD
analyze files in parallel

7. the main loop of PMD
but.. (shared as local)

8. the main loop of PMD
hmm.. can’t we “localize” ctx?

9. the main loop of PMD
not so fast.. (happens sometimes, deep down the stack)

10. the main loop of PMD
no cloning =>

11. the main loop of PMD
reuse

12. the main loop of PMD
to summarize..

13. the main loop of PMD
to summarize..
available parallelism

14. the main loop of PMD to summarize.. available parallelism
missed by write-set approach

15. Janus: sequence-based detection
/* offline */

16. Janus: sequence-based detection
/* offline */ T1
ctx.sourceCodeFilename = …; …
String fname=ctx.sourceCodeFilename; T2

17. Janus: sequence-based detection
/* offline */ T1
ctx.sourceCodeFilename = …; …
String fname=ctx.sourceCodeFilename; T2

18. Janus: sequence-based detection
/* offline */ T1
ctx.sourceCodeFilename = …; …
ctx.setAttribute(s, …); T2
… = ctx.getAttribute(s);

19. Janus: sequence-based detection
/* offline */ T1
ctx.sourceCodeFilename = …; …
ctx.setAttribute(s, …); T2
… = ctx.getAttribute(s);

20. Janus: sequence-based detection
/* offline */ T1
ctx.sourceCodeFilename = …; …
ctx.setAttribute(s, …); T2
… = ctx.getAttribute(s);

21. the main loop of PMD in paper: more real-world examples
more coding patterns encouraging refined
conflict detection

23. running example Reverse-Delete MST G’ = G
(e1 … en) = SortEdgesByDecreasingWeight()
for i=1 … n
G’.RemoveEdge(ei)
if (!G’.HasPath(ei.u, ei.v))
G’.AddEdge(ei)
return G’

24. running example ordered Reverse-Delete MST G’ = G
(e1 … en) = SortEdgesByDecreasingWeight()
for i=1 … n
G’.RemoveEdge(ei)
if (!G’.HasPath(ei.u, ei.v))
G’.AddEdge(ei)
return G’

25. running example heavy Reverse-Delete MST G’ = G
(e1 … en) = SortEdgesByDecreasingWeight()
for i=1 … n
G’.RemoveEdge(ei)
if (!G’.HasPath(ei.u, ei.v))
G’.AddEdge(ei)
return G’

26. running example identity if edge restored (available parallelism)
Reverse-Delete MST
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight()
for i=1 … n
G’.RemoveEdge(ei)
if (!G’.HasPath(ei.u, ei.v))
G’.AddEdge(ei)
return G’

27. take I: blind parallelism
Reverse-Delete MST
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight()
for i=1 … n
G’.RemoveEdge(ei)
if (!G’.HasPath(ei.u, ei.v))
G’.AddEdge(ei)
return G’
// broken  but fast 
parallel for
Give a concrete example.

28. take II: write-set speculation
/* with boosting */
Reverse-Delete MST
G’ = G
(e1 … en)= SortEdgesByDecreasingWeight()
for i=1 … n
G’.RemoveEdge(ei)
if (!G’.HasPath(ei.u, ei.v))
G’.AddEdge(ei)
return G’
Before we jump to boosting, we might want to talk about the concrete write-set approach.
@atomic // exploit graph semantics: node ≈ location

29. take II: write-set speculation
/* with boosting */
can we exploit the available parallelism with this approach?

30. the write-set approach in actionG: v1 v2 u T1 T2
Replace directed edges with lines.

31. the write-set approach in actionG: v1 v2 u T1 T2
RemoveEdge(u,v1)
RemoveEdge(u,v2)

32. the write-set approach in actionG: v1 v2 u T1 T2
RemoveEdge(u,v1)
RemoveEdge(u,v2)

33. the write-set approach in actionG: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)

34. the write-set approach in actionG: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)

35. the write-set approach in actionG: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b

36. the write-set approach in actionG: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)

37. the write-set approach in actionG: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)

38. take III: online commutativity checking
/* with boosting */
Reverse-Delete MST
G’ = G
(e1 … en)= SortEdgesByDecreasingWeight()
for i=1 … n
G’.RemoveEdge(ei)
if (!G’.HasPath(ei.u, ei.v))
G’.AddEdge(ei)
return G’
@atomic // full commutativity checking

39. precise checking in actionv1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)

40. precise checking in actionv1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)

41. precise checking in actionv1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
RemoveEdge(u,v1); HasPath(u,v1); AddEdge(u,v1) ∙
RemoveEdge(u,v2); HasPath(u,v2); AddEdge(u,v2) ≡
RemoveEdge(u,v2); HasPath(u,v2); AddEdge(u,v2) ∙
RemoveEdge(u,v1); HasPath(u,v1); AddEdge(u,v1)

42. precise checking in actionv1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
RemoveEdge(u,v1); HasPath(u,v1); AddEdge(u,v1) ∙
RemoveEdge(u,v2); HasPath(u,v2); AddEdge(u,v2) ≡
RemoveEdge(u,v2); HasPath(u,v2); AddEdge(u,v2) ∙
RemoveEdge(u,v1); HasPath(u,v1); AddEdge(u,v1)

43. but.. can we really afford this?G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)

44. but.. can we really afford this?G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
expensive instrumentation

45. but.. can we really afford this?G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2) +
expensive instrumentation

46. but.. can we really afford this?G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2) +
expensive instrumentation
expensive conflict detection

47. but.. can we really afford this?G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2) +
expensive instrumentation
expensive conflict detection

48. but.. can we really afford this?G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2) +
expensive instrumentation
expensive conflict detection
poor performance 

49. take III: Janus /* with boosting */ Reverse-Delete MST G’ = G
(e1 … en)= SortEdgesByDecreasingWeight()
for i=1 … n
G’.RemoveEdge(ei)
if (!G’.HasPath(ei.u, ei.v))
G’.AddEdge(ei)
return G’
@atomic // accurate checking utilizing offline data!

50. the Janus approach G: v1 v2 u T1 T2 RemoveEdge(u,v1) b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)

51. sequence-based detection
the Janus approach G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
sequence-based detection

52. sequence-based detection
the Janus approach G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
sequence-based detection
=> accurate

53. the Janus approach => accurate G: v1 v2 u T1 T2 RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
sequence-based detection
=> accurate
candidate sequences learned ahead of production runs, in profiling

54. the Janus approach => accurate G: v1 v2 u T1 T2 RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
sequence-based detection
=> accurate
candidate sequences learned ahead of production runs, in profiling
commutativity testing done offline

55. the Janus approach => accurate => cheap G: v1 v2 u T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
sequence-based detection
=> accurate
candidate sequences gleaned ahead of production runs, in profiling
=> cheap
commutativity testing done offline

57. the Janus flow
offline

58. find candidate sequences via profiling
the Janus flow
find candidate sequences via profiling
*ST runs
*single locations
offline

59. low instrumentation overhead
the Janus flow
find candidate sequences via profiling
*ST runs
*single locations
offline
low instrumentation overhead

60. offline the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
offline

61. offline the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline

62. cheap conflict detection
the Janus flow
find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
cheap conflict detection

63. offline the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline

64. offline online the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
online

65. offline online the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
consult sequence cache when attempting to commit a transaction
online

66. offline online the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
consult sequence cache when attempting to commit a transaction
perform write-set detection
miss
online

67. online the Janus flow soundness
consult sequence cache when attempting to commit a transaction
perform write-set detection
miss
online

69. offline online the Janus flow user input
find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
consult sequence cache when attempting to commit a transaction
perform write-set detection
miss
online

70. the Janus spec: data
n1:v1
n2:v2
n3:v2
n4:v1

71. the Janus spec: data n1:v1 n2:v2 n3:v2 n4:v1 node data n1 v1 …
neighbor
node n2 n1 n3 …
n4:v1

72. the Janus spec: operations
n1:v1
node
data n1 V1 n2 v2
n2:v2

73. the Janus spec: operations
n1:v1
node
data n1 V1 n2 v2
n2:v2
public void addnode(n)
{ nodes.add(n); }

74. the Janus spec: operations
n1:v1
node
data n1 V1 n2 v2
n2:v2
public void addnode(n)
{ nodes.add(n); }
insert <n.d,n>
into “nodes”

75. the Janus spec: operations
n1:v1
node
data n1 V1 n2 v2
n2:v2
public void addnode(n)
{ nodes.add(n); }
insert <n.d,n>
into “nodes”
n1:v1
n2:v2
n.d:n

76. the Janus spec: operations
n1:v1
node
data n1 V1 n2 v2
n2:v2
public void addnode(n)
{ nodes.add(n); }
insert <n.d,n>
into “nodes”
n1:v1
node
data n1 v1 n2 v2 n
n.d
n2:v2
n.d:n

77. offline online the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
consult sequence cache when attempting to commit a transaction
perform write-set detection
miss
online

78. sequence mining G: v1 v2 u
Explain that per-location sequences are mined, the motivating being to be able to utilize the write-set instrumentation scheme.

79. sequence mining G: v1 v2 u
G’ = G

80. sequence mining G: v1 v2 u G’ = G
(e1 … en) = SortEdgesByDecreasingWeight()

81. sequence mining G: v1 v2 u G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1
RemoveEdge(u,v1)

82. sequence mining G: v1 v2 u G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1
RemoveEdge(u,v1)
b = HasPath(u,v1)

83. RemoveEdge/T1; HasPath/T1
sequence mining G: v1 v2 u
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1
RemoveEdge(u,v1)
b = HasPath(u,v1)
RemoveEdge/T1; HasPath/T1

84. RemoveEdge/T1; HasPath/T1
sequence mining G: v1 v2 u
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
RemoveEdge/T1; HasPath/T1

85. RemoveEdge/T1; HasPath/T1
sequence mining G: v1 v2 u
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge/T1; HasPath/T1

86. RemoveEdge/T1; HasPath/T1; AddEdge/T1
sequence mining G: v1 v2 u
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge/T1; HasPath/T1; AddEdge/T1

87. RemoveEdge/T1; HasPath/T1; AddEdge/T1
sequence mining G: v1 v2 u
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
RemoveEdge/T1; HasPath/T1; AddEdge/T1

88. RemoveEdge/T1; HasPath/T1; AddEdge/T1
sequence mining G: v1 v2 u
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
RemoveEdge/T1; HasPath/T1; AddEdge/T1
RemoveEdge/T2

89. RemoveEdge/T1; HasPath/T1; AddEdge/T1
sequence mining G: v1 v2 u
G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
RemoveEdge/T1; HasPath/T1; AddEdge/T1
RemoveEdge/T2

90. sequence mining G: v1 v2 u G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
RemoveEdge/T1; HasPath/T1; AddEdge/T1
RemoveEdge/T2; HasPath/T2

91. sequence mining G: v1 v2 u G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
RemoveEdge/T1; HasPath/T1; AddEdge/T1
RemoveEdge/T2; HasPath/T2

92. sequence mining G: v1 v2 u G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
RemoveEdge/T1; HasPath/T1; AddEdge/T1
RemoveEdge/T2; HasPath/T2; AddEdge/T2

93. sequence mining G: v1 v2 u G’ = G
(e1 … en) = SortEdgesByDecreasingWeight() T1 T2
RemoveEdge(u,v1)
b = HasPath(u,v1)
test b
AddEdge(u,v1)
RemoveEdge(u,v2)
b = HasPath(u,v2)
test b
AddEdge(u,v2)
return G’
RemoveEdge/T1; HasPath/T1; AddEdge/T1
RemoveEdge/T2; HasPath/T2; AddEdge/T2

94. offline online the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
consult sequence cache when attempting to commit a transaction
perform write-set detection
miss
online

95. commutativity checking
RemoveEdge(u,v1); HasPath(u,v1); AddEdge(u,v1)
RemoveEdge(u,v2); HasPath(u,v2); AddEdge(u,v2) ∙

96. commutativity checking
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z) ∙

97. commutativity checking
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z) ∙
ɸ0 = encode(G)
ɸ1 = ɸ0 ˄ {x,y} ϵ “edges” …
ɸk = ɸk-1 ˄ {x,y} ϵ “edges”
ɸk+1 = ɸk ˄ {x,y} ϵ “edges”
ɸm = ɸm-1 ˄ {x,y} ϵ “edges”
ψ0 = encode(G)
ψ1 = ψ0 ˄ {x,z} ϵ “edges”
ψk = ψk-1 ˄ {x,z} ϵ “edges”
ψk+1 = ψk ˄ {x,y} ϵ “edges”
ψm = ψm-1 ˄ {x,y} ϵ “edges”

98. commutativity checking
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z) ∙
ɸ0 = encode(G)
ɸ1 = ɸ0 ˄ {x,y} ϵ “edges” …
ɸk = ɸk-1 ˄ {x,y} ϵ “edges”
ɸk+1 = ɸk ˄ {x,y} ϵ “edges”
ɸm = ɸm-1 ˄ {x,y} ϵ “edges”
ψ0 = encode(G)
ψ1 = ψ0 ˄ {x,z} ϵ “edges”
ψk = ψk-1 ˄ {x,z} ϵ “edges”
ψk+1 = ψk ˄ {x,y} ϵ “edges”
ψm = ψm-1 ˄ {x,y} ϵ “edges”
test: ¬(ѱ ≡ ɸ)

99. commutativity checking
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z) ∙
in paper:
encoding rules
“projection” : sound commutativity checking
for sequences induced by individual memory
locations
ɸ0 = encode(G)
ɸ1 = ɸ0 ˄ {x,y} ϵ “edges” …
ɸk = ɸk-1 ˄ {x,y} ϵ “edges”
ɸk+1 = ɸk ˄ {x,y} ϵ “edges”
ɸm = ɸm-1 ˄ {x,y} ϵ “edges”
ψ0 = encode(G)
ψ1 = ψ0 ˄ {x,z} ϵ “edges”
ψk = ψk-1 ˄ {x,z} ϵ “edges”
ψk+1 = ψk ˄ {x,y} ϵ “edges”
ψm = ψm-1 ˄ {x,y} ϵ “edges”
test: ¬(ѱ ≡ ɸ)

100. offline online the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
consult sequence cache when attempting to commit a transaction
perform write-set detection
miss
online

101. generalization S1= S2= RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z)
S2=

102. generalization S1= S2= [S1 S2]G // “concrete” seq.s commute
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
S1=
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z)
S2=
[S1 S2]G // “concrete” seq.s commute

103. generalization S1= S2= [S1 S2]G // “concrete” seq.s commute
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
S1=
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z)
S2=
[S1 S2]G // “concrete” seq.s commute
S1(G) = S1∙S1(G) // idempotent
S2(G) = S2∙S2(G) // idempotent

104. generalization S1= S2= [S1 S2]G // “concrete” seq.s commute
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
S1=
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z)
S2=
[S1 S2]G // “concrete” seq.s commute
S1(G) = S1∙S1(G) // idempotent
S2(G) = S2∙S2(G) // idempotent

105. generalization S1= S2= [S1 S2]G // “concrete” seq.s commute
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
S1=
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z)
S2=
[S1 S2]G // “concrete” seq.s commute
S1(G) = S1∙S1(G) // idempotent
S2(G) = S2∙S2(G) // idempotent
[S1+ S2+]G // Kleene-cross generalization

106. generalization + + S1= S2= [S1 S2]G // “concrete” seq.s commute
RemoveEdge(x,y); HasPath(x,y); AddEdge(x,y)
S1= +
RemoveEdge(x,z); HasPath(x,z); AddEdge(x,z)
S2=
[S1 S2]G // “concrete” seq.s commute
S1(G) = S1∙S1(G) // idempotent
S2(G) = S2∙S2(G) // idempotent
[S1+ S2+]G // Kleene-cross generalization

107. offline online the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
specialized spec
offline
consult sequence cache when attempting to commit a transaction
perform write-set detection
miss
online

108. specialized commutativity spec
state
operation

109. specialized commutativity spec
state
operation
any
AddEdge(x,z)/ff
AddEdge(x,y)/tt

110. specialized commutativity spec
state
operation
any
AddEdge(x,z)/ff
AddEdge(x,y)/tt
HasPath(z,w)/tt
AddEdge(x,y)/ff

111. specialized commutativity spec
state
operation
any
AddEdge(x,z)/ff
AddEdge(x,y)/tt
HasPath(z,w)/tt
AddEdge(x,y)/ff …

112. specialized commutativity spec
state
operation
any
AddEdge(x,z)/ff
AddEdge(x,y)/tt
HasPath(z,w)/tt
AddEdge(x,y)/ff …
[ RemoveEdge(x,z)/tt; HasPath(x,z)/ff; AddEdge(x,z)/tt ]+
[ RemoveEdge(x,y)/tt; HasPath(x,y)/ff; AddEdge(x,y)/tt ]+

113. specialized commutativity spec
state
operation
any
AddEdge(x,z)/ff
AddEdge(x,y)/tt
HasPath(z,w)/tt
AddEdge(x,y)/ff …
[ RemoveEdge(x,z)/tt; HasPath(x,z)/ff; AddEdge(x,z)/tt ]+
[ RemoveEdge(x,y)/tt; HasPath(x,y)/ff; AddEdge(x,y)/tt ]+
tailored for given application

114. offline online the Janus flow find candidate sequences via profiling
*ST runs
*single locations
test sequences for commutativity
cache (generalized version) of commutative sequences
offline
consult sequence cache when attempting to commit a transaction
perform write-set detection
miss
online

115. parallelization protocol
shared state [k] a1 a2 . an
privatized state [k] b1 b2 . bm
shared state [k+1]
privatized state [k’]
shared state [k+2]
(a1 … an) ∙ (b1 … bm) Sk ≡ (b1 … bm) ∙ (a1 … an) Sk

116. parallelization protocol
shared state [k] a1 a2 . an
privatized state [k] b1 b2 . bm
shared state [k+1]
privatized state [k’]
shared state [k+2]
(a1 … an) ∙ (b1 … bm) Sk ≡ (b1 … bm) ∙ (a1 … an) Sk

117. parallelization protocol
shared state [k]
privatized state [k]
shared state [k+1]
privatized state [k’]
shared state [k+2]
replay
on shared
state bi
shared state [k+3]

118. parallelization protocol
shared state [k] a1 a2 . an
privatized state [k] b1 b2 . bm
shared state [k+1]
privatized state [k’]
shared state [k+2]
(a1 … an) ∙ (b1 … bm) Sk ≡ (b1 … bm) ∙ (a1 … an) Sk

119. parallelization protocol
shared state [k]
privatized state [k+2]
shared state [k+1]
retry
shared state [k+2]

120. parallel reverse-delete MST

121. parallel reverse-delete MST

122. parallel reverse-delete MST

123. parallel reverse-delete MST

124. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)

125. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)

126. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)
RemoveEdge(u,v2)
HasPath(u,v2)
AddEdge(u,v2)

127. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)
RemoveEdge(u,v2)
HasPath(u,v2)
AddEdge(u,v2)

128. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)
RemoveEdge(u,v2)
HasPath(u,v2)
AddEdge(u,v2)
RemoveEdge(u,v3)
HasPath(u,v3)
AddEdge(u,v3)

129. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)
RemoveEdge(u,v3)
HasPath(u,v3)
AddEdge(u,v3)
RemoveEdge(u,v2)
HasPath(u,v2)
AddEdge(u,v2) ?

130. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)
RemoveEdge(u,v3)
HasPath(u,v3)
AddEdge(u,v3)
RemoveEdge(u,v2)
HasPath(u,v2)
AddEdge(u,v2)
x.f = y; ||
z = x.f;

131. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)
RemoveEdge(u,v3)
HasPath(u,v3)
AddEdge(u,v3)
RemoveEdge(u,v2)
HasPath(u,v2)
AddEdge(u,v2)
dope!

132. parallel reverse-delete MSTb
state
operation …
any
[ RemoveEdge(x,z)/tt; HasPath(x,z)/ff; AddEdge(x,z)/tt ]+
[ RemoveEdge(x,y)/tt; HasPath(x,y)/ff; AddEdge(x,y)/tt ]+
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)
RemoveEdge(u,v3)
HasPath(u,v3)
AddEdge(u,v3)
RemoveEdge(u,v2)
HasPath(u,v2)
AddEdge(u,v2)

133. parallel reverse-delete MST
RemoveEdge(u,v1)
HasPath(u,v1)
AddEdge(u,v1)
RemoveEdge(u,v3)
HasPath(u,v3)
AddEdge(u,v3)
RemoveEdge(u,v2)
HasPath(u,v2)
AddEdge(u,v2)
that’s better..

135. benchmarks description name
utility for synchronizing pairs of directories
JFileSync
greedy graph-coloring algorithm
JGraphT-1 (coloring)
saturation-degree node-ordering algorithm for heuristic graph coloring
JGraphT-2 (ordering)
Java source code analyzer
PMD
machine-learning library for data-mining tasks
Weka

136. JGraphT-1
JGraphT-2
speedup
PMD
JFileSync
Weka

137. retries
JGraphT-1
JGraphT-2
PMD
JFileSync
Weka

138. misses W/ seq. abstraction W/O seq. abstraction JGraphT-2 JGraphT-1
JFileSync
PMD
Weka
W/ seq. abstraction
W/O seq. abstraction

139. conclusions profiling is useful for speculation
prevalent behaviors (dependencies) => effective specialization
speculation = safety net
Janus enables practical accuracy boosting
lifts commutativity checking to sequences of operations
runtime overhead comparable to write-set approach