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 14 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 14 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 14 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 14 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 14 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 14 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 14 mins ago
ShahidShahid
101
asked 14 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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