The process to calculate the Levenshtien distance of each element of a large data set with every other...
0
$begingroup$
I am trying to optimize the process to calculate the Levenshtien distance of each element of a huge list of data called "ListA" with every other element of another list called "ListB". However the code that I have written for this process takes a lot of time when I increase the data in any of the datasets. I need to optimize the process for huge data sets. Please advise where do I need to make the improvements.
//Split the ListA data to smaller chunks and loop through those chunks
var splitGroupSize = 1000;
var sourceDataBatchesCount = ListA.Count / splitGroupSize;
// Loop through the smaller chunks
for (int b = 0; b < sourceDataBatchesCount; b++)
{
var currentBatchMatchedWords = new List<Tuple<long, string, string, string, string, string, double>>();
int skipRowCount = b * splitGroupSize;
int takeRowCount = splitGroupSize;
// Get chunks of data from ListA according to the skipRowCount and takeRowCount
var currentSourceDataBatch = FuzzyMatchRepository.FetchSourceDataBatch(skipRowCount, takeRowCount);
//Loop through the ListB and parallely calculate the distance between chunks of List A and List B data
for (int i = 0; i < ListB.Count; i++)
{
Parallel.For(
0,
currentSourceDataBatch.Count,
new ParallelOptions { MaxDegreeOfParallelism = Environment.ProcessorCount * 10 },
cntr =>
{
try
{
// call the Levenshtien Algorithm to calculate the distance between each element of ListB and the smaller chunk of List A.
double similarity = LevenshteinDistance(currentSourceDataBatch[cntr], ListB[i]);
if (similarity >= 70)
{
// save the data in tuple.
}
cntr++;
}
catch (Exception ex)
{
exceptions.Enqueue(ex);
}
});
}
}
c# performance
asked 4 mins ago
ShahidShahid
101
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0
$begingroup$
I am trying to optimize the process to calculate the Levenshtien distance of each element of a huge list of data called "ListA" with every other element of another list called "ListB". However the code that I have written for this process takes a lot of time when I increase the data in any of the datasets. I need to optimize the process for huge data sets. Please advise where do I need to make the improvements.
//Split the ListA data to smaller chunks and loop through those chunks
var splitGroupSize = 1000;
var sourceDataBatchesCount = ListA.Count / splitGroupSize;
// Loop through the smaller chunks
for (int b = 0; b < sourceDataBatchesCount; b++)
{
var currentBatchMatchedWords = new List<Tuple<long, string, string, string, string, string, double>>();
int skipRowCount = b * splitGroupSize;
int takeRowCount = splitGroupSize;
// Get chunks of data from ListA according to the skipRowCount and takeRowCount
var currentSourceDataBatch = FuzzyMatchRepository.FetchSourceDataBatch(skipRowCount, takeRowCount);
//Loop through the ListB and parallely calculate the distance between chunks of List A and List B data
for (int i = 0; i < ListB.Count; i++)
{
Parallel.For(
0,
currentSourceDataBatch.Count,
new ParallelOptions { MaxDegreeOfParallelism = Environment.ProcessorCount * 10 },
cntr =>
{
try
{
// call the Levenshtien Algorithm to calculate the distance between each element of ListB and the smaller chunk of List A.
double similarity = LevenshteinDistance(currentSourceDataBatch[cntr], ListB[i]);
if (similarity >= 70)
{
// save the data in tuple.
}
cntr++;
}
catch (Exception ex)
{
exceptions.Enqueue(ex);
}
});
}
}
c# performance
asked 4 mins ago
ShahidShahid
101
New contributor
Shahid is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
I am trying to optimize the process to calculate the Levenshtien distance of each element of a huge list of data called "ListA" with every other element of another list called "ListB". However the code that I have written for this process takes a lot of time when I increase the data in any of the datasets. I need to optimize the process for huge data sets. Please advise where do I need to make the improvements.
//Split the ListA data to smaller chunks and loop through those chunks
var splitGroupSize = 1000;
var sourceDataBatchesCount = ListA.Count / splitGroupSize;
// Loop through the smaller chunks
for (int b = 0; b < sourceDataBatchesCount; b++)
{
var currentBatchMatchedWords = new List<Tuple<long, string, string, string, string, string, double>>();
int skipRowCount = b * splitGroupSize;
int takeRowCount = splitGroupSize;
// Get chunks of data from ListA according to the skipRowCount and takeRowCount
var currentSourceDataBatch = FuzzyMatchRepository.FetchSourceDataBatch(skipRowCount, takeRowCount);
//Loop through the ListB and parallely calculate the distance between chunks of List A and List B data
for (int i = 0; i < ListB.Count; i++)
{
Parallel.For(
0,
currentSourceDataBatch.Count,
new ParallelOptions { MaxDegreeOfParallelism = Environment.ProcessorCount * 10 },
cntr =>
{
try
{
// call the Levenshtien Algorithm to calculate the distance between each element of ListB and the smaller chunk of List A.
double similarity = LevenshteinDistance(currentSourceDataBatch[cntr], ListB[i]);
if (similarity >= 70)
{
// save the data in tuple.
}
cntr++;
}
catch (Exception ex)
{
exceptions.Enqueue(ex);
}
});
}
}
c# performance
asked 4 mins ago
ShahidShahid
101
New contributor
Shahid is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to optimize the process to calculate the Levenshtien distance of each element of a huge list of data called "ListA" with every other element of another list called "ListB". However the code that I have written for this process takes a lot of time when I increase the data in any of the datasets. I need to optimize the process for huge data sets. Please advise where do I need to make the improvements.
//Split the ListA data to smaller chunks and loop through those chunks
var splitGroupSize = 1000;
var sourceDataBatchesCount = ListA.Count / splitGroupSize;
// Loop through the smaller chunks
for (int b = 0; b < sourceDataBatchesCount; b++)
{
var currentBatchMatchedWords = new List<Tuple<long, string, string, string, string, string, double>>();
int skipRowCount = b * splitGroupSize;
int takeRowCount = splitGroupSize;
// Get chunks of data from ListA according to the skipRowCount and takeRowCount
var currentSourceDataBatch = FuzzyMatchRepository.FetchSourceDataBatch(skipRowCount, takeRowCount);
//Loop through the ListB and parallely calculate the distance between chunks of List A and List B data
for (int i = 0; i < ListB.Count; i++)
{
Parallel.For(
0,
currentSourceDataBatch.Count,
new ParallelOptions { MaxDegreeOfParallelism = Environment.ProcessorCount * 10 },
cntr =>
{
try
{
// call the Levenshtien Algorithm to calculate the distance between each element of ListB and the smaller chunk of List A.
double similarity = LevenshteinDistance(currentSourceDataBatch[cntr], ListB[i]);
if (similarity >= 70)
{
// save the data in tuple.
}
cntr++;
}
catch (Exception ex)
{
exceptions.Enqueue(ex);
}
});
}
}
c# performance
c# performance
asked 4 mins ago
ShahidShahid
101
New contributor
Shahid is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
ShahidShahid
101
New contributor
Shahid is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
ShahidShahid
101
New contributor
Shahid is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 4 mins ago
ShahidShahid
101
asked 4 mins ago
ShahidShahid
101
101
New contributor
Shahid is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Shahid is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Shahid is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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