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Why does a table with a defined constant in its index compute 10X slower?
Why does array packing in Table behave like this?Why is building a table of function values so much slower than just plotting the function?Issue with very large lists in MathematicaWhy does `Table` returns values but `Plot` doesn't plot them?How to speed up computation of medoids?Why is CompilationTarget -> C slower than directly writing with C?ParallelTable much slower than Table on RandomReal with arbitrary precisionPart does not exist in table construction, but I don't explicitly index that highwhy Findroot slower with jacobian?Why does Table[] slow down exponentially with increasing length?
$begingroup$
I need to do some iterative summations. Here is a minimum working example:
data = Table[RandomReal[], x, 1, 1000000];
(* Method 1 *)
Timing[Total[Table[Total[ Table[data[[i]], i, j, 10 + j]], j, 1, Length[data] - 5*10]]]
(* Method 2, with constant index *)
m = 10;
Timing[Total[Table[Total[ Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*m]]]
And here are the outputs:
0.5625, 5.49936*10^6
9.28125, 5.49936*10^6
For some reason, using m=10 makes it much slower. I will need to do a bunch of m's, so this is the bottom of a larger nest.
What is a faster way to do this?
Late Edit:
Bonus question: How to optimize this one as well:
Timing[Total[Table[ (Total[ Table[data[[i]], i, j, m + j]])^2 , j, 1, Length[data] - 5*m]]]
list-manipulation performance-tuning table
$endgroup$
add a comment |
$begingroup$
I need to do some iterative summations. Here is a minimum working example:
data = Table[RandomReal[], x, 1, 1000000];
(* Method 1 *)
Timing[Total[Table[Total[ Table[data[[i]], i, j, 10 + j]], j, 1, Length[data] - 5*10]]]
(* Method 2, with constant index *)
m = 10;
Timing[Total[Table[Total[ Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*m]]]
And here are the outputs:
0.5625, 5.49936*10^6
9.28125, 5.49936*10^6
For some reason, using m=10 makes it much slower. I will need to do a bunch of m's, so this is the bottom of a larger nest.
What is a faster way to do this?
Late Edit:
Bonus question: How to optimize this one as well:
Timing[Total[Table[ (Total[ Table[data[[i]], i, j, m + j]])^2 , j, 1, Length[data] - 5*m]]]
list-manipulation performance-tuning table
$endgroup$
add a comment |
$begingroup$
I need to do some iterative summations. Here is a minimum working example:
data = Table[RandomReal[], x, 1, 1000000];
(* Method 1 *)
Timing[Total[Table[Total[ Table[data[[i]], i, j, 10 + j]], j, 1, Length[data] - 5*10]]]
(* Method 2, with constant index *)
m = 10;
Timing[Total[Table[Total[ Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*m]]]
And here are the outputs:
0.5625, 5.49936*10^6
9.28125, 5.49936*10^6
For some reason, using m=10 makes it much slower. I will need to do a bunch of m's, so this is the bottom of a larger nest.
What is a faster way to do this?
Late Edit:
Bonus question: How to optimize this one as well:
Timing[Total[Table[ (Total[ Table[data[[i]], i, j, m + j]])^2 , j, 1, Length[data] - 5*m]]]
list-manipulation performance-tuning table
$endgroup$
I need to do some iterative summations. Here is a minimum working example:
data = Table[RandomReal[], x, 1, 1000000];
(* Method 1 *)
Timing[Total[Table[Total[ Table[data[[i]], i, j, 10 + j]], j, 1, Length[data] - 5*10]]]
(* Method 2, with constant index *)
m = 10;
Timing[Total[Table[Total[ Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*m]]]
And here are the outputs:
0.5625, 5.49936*10^6
9.28125, 5.49936*10^6
For some reason, using m=10 makes it much slower. I will need to do a bunch of m's, so this is the bottom of a larger nest.
What is a faster way to do this?
Late Edit:
Bonus question: How to optimize this one as well:
Timing[Total[Table[ (Total[ Table[data[[i]], i, j, m + j]])^2 , j, 1, Length[data] - 5*m]]]
list-manipulation performance-tuning table
list-manipulation performance-tuning table
edited 1 hour ago
Carl Woll
79.1k3102206
79.1k3102206
asked 4 hours ago
axsvl77axsvl77
405315
405315
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
The problem lies mostly in the inner Table:
Timing[Total[Table[Total[Table[data[[i]], i, j, 10 + j]], j, 1,Length[data] - 5*10]]]
m = 10;
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*10]]]
0.366407, 5.50276*10^6
8.01738, 5.50276*10^6
I think the reason is this:
Because the global variable m could theoretically change its value during the computions, the body of the outer table cannot be compiled (without calls to MainEvaluate). At least, the JIT compiler does not analyze the body of the outer loop thoroughly enough to decide that m won't change.
You can help the JIT compiler by using With:
With[m = 10,
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1,Length[data] - 5*m]]]
]
0.369601, 5.5049*10^6
Addendum:
By focusing on the post's title, I have completely overlooked the question on how to make it faster. Here is my proposal (c) vs. the OP's one (a) and Carl's (b):
a = With[m = 10,
Total[
Table[Total[Table[data[[i]], i, j, m + j]], j, 1,
Length[data] - 5*m]]
]; // RepeatedTiming // First
b = Total@ListCorrelate[ConstantArray[1., m + 1],
data[[;; -50 + m - 1]]]; // RepeatedTiming // First
c = Plus[
Range[1., m].data[[1 ;; m]],
(m + 1) Total[data[[m + 1 ;; -5*m - 1]]],
Range[N@m, 1., -1].data[[-5 m ;; -4 m - 1]]
]; // RepeatedTiming // First
a == b == c
0.28
0.017
0.0018
True
$endgroup$
add a comment |
$begingroup$
You can use ListCorrelate:
m=10;
Total @ ListCorrelate[ConstantArray[1,m+1], data[[;;-4 m-1]]] //AbsoluteTiming
0.017725, 5.50044*10^6
Bonus question
For the bonus question:
data = RandomReal[1, 10^5];
Your version:
With[m = 10,
Total[Table[(Total[Table[data[[i]],i,j,m+j]])^2,j,1,Length[data]-5*m]]
] //AbsoluteTiming
0.448739, 3.11778*10^7
Using ListCorrelate again:
m = 10;
#.#& @ ListCorrelate[ConstantArray[1, m+1], data[[ ;; -4 m - 1]]] //AbsoluteTiming
0.018401, 3.11778*10^7
$endgroup$
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
The problem lies mostly in the inner Table:
Timing[Total[Table[Total[Table[data[[i]], i, j, 10 + j]], j, 1,Length[data] - 5*10]]]
m = 10;
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*10]]]
0.366407, 5.50276*10^6
8.01738, 5.50276*10^6
I think the reason is this:
Because the global variable m could theoretically change its value during the computions, the body of the outer table cannot be compiled (without calls to MainEvaluate). At least, the JIT compiler does not analyze the body of the outer loop thoroughly enough to decide that m won't change.
You can help the JIT compiler by using With:
With[m = 10,
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1,Length[data] - 5*m]]]
]
0.369601, 5.5049*10^6
Addendum:
By focusing on the post's title, I have completely overlooked the question on how to make it faster. Here is my proposal (c) vs. the OP's one (a) and Carl's (b):
a = With[m = 10,
Total[
Table[Total[Table[data[[i]], i, j, m + j]], j, 1,
Length[data] - 5*m]]
]; // RepeatedTiming // First
b = Total@ListCorrelate[ConstantArray[1., m + 1],
data[[;; -50 + m - 1]]]; // RepeatedTiming // First
c = Plus[
Range[1., m].data[[1 ;; m]],
(m + 1) Total[data[[m + 1 ;; -5*m - 1]]],
Range[N@m, 1., -1].data[[-5 m ;; -4 m - 1]]
]; // RepeatedTiming // First
a == b == c
0.28
0.017
0.0018
True
$endgroup$
add a comment |
$begingroup$
The problem lies mostly in the inner Table:
Timing[Total[Table[Total[Table[data[[i]], i, j, 10 + j]], j, 1,Length[data] - 5*10]]]
m = 10;
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*10]]]
0.366407, 5.50276*10^6
8.01738, 5.50276*10^6
I think the reason is this:
Because the global variable m could theoretically change its value during the computions, the body of the outer table cannot be compiled (without calls to MainEvaluate). At least, the JIT compiler does not analyze the body of the outer loop thoroughly enough to decide that m won't change.
You can help the JIT compiler by using With:
With[m = 10,
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1,Length[data] - 5*m]]]
]
0.369601, 5.5049*10^6
Addendum:
By focusing on the post's title, I have completely overlooked the question on how to make it faster. Here is my proposal (c) vs. the OP's one (a) and Carl's (b):
a = With[m = 10,
Total[
Table[Total[Table[data[[i]], i, j, m + j]], j, 1,
Length[data] - 5*m]]
]; // RepeatedTiming // First
b = Total@ListCorrelate[ConstantArray[1., m + 1],
data[[;; -50 + m - 1]]]; // RepeatedTiming // First
c = Plus[
Range[1., m].data[[1 ;; m]],
(m + 1) Total[data[[m + 1 ;; -5*m - 1]]],
Range[N@m, 1., -1].data[[-5 m ;; -4 m - 1]]
]; // RepeatedTiming // First
a == b == c
0.28
0.017
0.0018
True
$endgroup$
add a comment |
$begingroup$
The problem lies mostly in the inner Table:
Timing[Total[Table[Total[Table[data[[i]], i, j, 10 + j]], j, 1,Length[data] - 5*10]]]
m = 10;
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*10]]]
0.366407, 5.50276*10^6
8.01738, 5.50276*10^6
I think the reason is this:
Because the global variable m could theoretically change its value during the computions, the body of the outer table cannot be compiled (without calls to MainEvaluate). At least, the JIT compiler does not analyze the body of the outer loop thoroughly enough to decide that m won't change.
You can help the JIT compiler by using With:
With[m = 10,
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1,Length[data] - 5*m]]]
]
0.369601, 5.5049*10^6
Addendum:
By focusing on the post's title, I have completely overlooked the question on how to make it faster. Here is my proposal (c) vs. the OP's one (a) and Carl's (b):
a = With[m = 10,
Total[
Table[Total[Table[data[[i]], i, j, m + j]], j, 1,
Length[data] - 5*m]]
]; // RepeatedTiming // First
b = Total@ListCorrelate[ConstantArray[1., m + 1],
data[[;; -50 + m - 1]]]; // RepeatedTiming // First
c = Plus[
Range[1., m].data[[1 ;; m]],
(m + 1) Total[data[[m + 1 ;; -5*m - 1]]],
Range[N@m, 1., -1].data[[-5 m ;; -4 m - 1]]
]; // RepeatedTiming // First
a == b == c
0.28
0.017
0.0018
True
$endgroup$
The problem lies mostly in the inner Table:
Timing[Total[Table[Total[Table[data[[i]], i, j, 10 + j]], j, 1,Length[data] - 5*10]]]
m = 10;
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1, Length[data] - 5*10]]]
0.366407, 5.50276*10^6
8.01738, 5.50276*10^6
I think the reason is this:
Because the global variable m could theoretically change its value during the computions, the body of the outer table cannot be compiled (without calls to MainEvaluate). At least, the JIT compiler does not analyze the body of the outer loop thoroughly enough to decide that m won't change.
You can help the JIT compiler by using With:
With[m = 10,
Timing[Total[Table[Total[Table[data[[i]], i, j, m + j]], j, 1,Length[data] - 5*m]]]
]
0.369601, 5.5049*10^6
Addendum:
By focusing on the post's title, I have completely overlooked the question on how to make it faster. Here is my proposal (c) vs. the OP's one (a) and Carl's (b):
a = With[m = 10,
Total[
Table[Total[Table[data[[i]], i, j, m + j]], j, 1,
Length[data] - 5*m]]
]; // RepeatedTiming // First
b = Total@ListCorrelate[ConstantArray[1., m + 1],
data[[;; -50 + m - 1]]]; // RepeatedTiming // First
c = Plus[
Range[1., m].data[[1 ;; m]],
(m + 1) Total[data[[m + 1 ;; -5*m - 1]]],
Range[N@m, 1., -1].data[[-5 m ;; -4 m - 1]]
]; // RepeatedTiming // First
a == b == c
0.28
0.017
0.0018
True
edited 2 hours ago
answered 4 hours ago
Henrik SchumacherHenrik Schumacher
62.4k586173
62.4k586173
add a comment |
add a comment |
$begingroup$
You can use ListCorrelate:
m=10;
Total @ ListCorrelate[ConstantArray[1,m+1], data[[;;-4 m-1]]] //AbsoluteTiming
0.017725, 5.50044*10^6
Bonus question
For the bonus question:
data = RandomReal[1, 10^5];
Your version:
With[m = 10,
Total[Table[(Total[Table[data[[i]],i,j,m+j]])^2,j,1,Length[data]-5*m]]
] //AbsoluteTiming
0.448739, 3.11778*10^7
Using ListCorrelate again:
m = 10;
#.#& @ ListCorrelate[ConstantArray[1, m+1], data[[ ;; -4 m - 1]]] //AbsoluteTiming
0.018401, 3.11778*10^7
$endgroup$
add a comment |
$begingroup$
You can use ListCorrelate:
m=10;
Total @ ListCorrelate[ConstantArray[1,m+1], data[[;;-4 m-1]]] //AbsoluteTiming
0.017725, 5.50044*10^6
Bonus question
For the bonus question:
data = RandomReal[1, 10^5];
Your version:
With[m = 10,
Total[Table[(Total[Table[data[[i]],i,j,m+j]])^2,j,1,Length[data]-5*m]]
] //AbsoluteTiming
0.448739, 3.11778*10^7
Using ListCorrelate again:
m = 10;
#.#& @ ListCorrelate[ConstantArray[1, m+1], data[[ ;; -4 m - 1]]] //AbsoluteTiming
0.018401, 3.11778*10^7
$endgroup$
add a comment |
$begingroup$
You can use ListCorrelate:
m=10;
Total @ ListCorrelate[ConstantArray[1,m+1], data[[;;-4 m-1]]] //AbsoluteTiming
0.017725, 5.50044*10^6
Bonus question
For the bonus question:
data = RandomReal[1, 10^5];
Your version:
With[m = 10,
Total[Table[(Total[Table[data[[i]],i,j,m+j]])^2,j,1,Length[data]-5*m]]
] //AbsoluteTiming
0.448739, 3.11778*10^7
Using ListCorrelate again:
m = 10;
#.#& @ ListCorrelate[ConstantArray[1, m+1], data[[ ;; -4 m - 1]]] //AbsoluteTiming
0.018401, 3.11778*10^7
$endgroup$
You can use ListCorrelate:
m=10;
Total @ ListCorrelate[ConstantArray[1,m+1], data[[;;-4 m-1]]] //AbsoluteTiming
0.017725, 5.50044*10^6
Bonus question
For the bonus question:
data = RandomReal[1, 10^5];
Your version:
With[m = 10,
Total[Table[(Total[Table[data[[i]],i,j,m+j]])^2,j,1,Length[data]-5*m]]
] //AbsoluteTiming
0.448739, 3.11778*10^7
Using ListCorrelate again:
m = 10;
#.#& @ ListCorrelate[ConstantArray[1, m+1], data[[ ;; -4 m - 1]]] //AbsoluteTiming
0.018401, 3.11778*10^7
edited 1 hour ago
answered 4 hours ago
Carl WollCarl Woll
79.1k3102206
79.1k3102206
add a comment |
add a comment |
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