Scientific Computation on JRuby

Scientific Computation on JRuby

• 1.
  Scientific Computation on JRuby PrasunAnand github.com/prasunanand
• 3.
  NMatrix NMatrix is Sciruby’snumerical matrix core implemented for dense and sparse matrices. NMatrix on ATLAS/CBLAS/CLAPACK and standard LAPACK. NMatrix for JRuby has been implemented using Apache Commons Math.
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  y = NMatrix.new([2,3],[1,2,3,4,5,6],dtype: :float32, stype: :dense) pp y [ [1,2,3] [4,5,6] ]
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  Optimization and Speed MemoryManagement Chaining java methods
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  Benchmark b = Java::double[15_000,15_000].new c= Java::double[15_000,15_000].new # b= Array.new(25000000) index=0 puts Benchmark.measure{ (0...15000).each do |i| (0...15000).each do |j| b[i][j] = index index+=1 end end } index =0 puts Benchmark.measure{ (0...15000).each do |i| (0...15000).each do |j| c[i][j] = b[i][j] index+=1 end end }
• 7.
  #prints #43.260000 3.250000 46.510000( 39.606356) #67.790000 0.070000 67.860000 ( 65.126546) #RAM consumed => 5.4GB
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  Chaining Java methods LargeArrays. Converting to two-d matrix from flat_array. Coercion. Each Fixnum object could be anywhere from 64 to 128 bytes in memory, depending on the platform and how the JVM lays the objects out in memory. So even on the low end, 100M numeric elements would be 6.4GB of objects. And then the Array itself will be at least 100M * 4-8 bytes, or at least 400MB. So there's at most 7.5GB difference from MRI.
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  Memory Management Overcopying Type Guessing Autoboxing
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  Speed Never upset theGarbage Collector. Speed improved 1000times (from 25s to 0.022s) :).
• 11.
  Mixed-models Mixed models arestatistical models which predict the value of a response variable as a result of fixed and random effects. It is like a Ruby version of lme4 (an R package). It has been successfully ported to JRuby too :) .
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  Matrix Multiplication
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  Matrix Addition
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  Matrix Subtraction
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  Thank You Github: prasunanand Twitter:@prasun_anand Blog : prasunanand.com