Benchmarking reading binary values from a file with F#, Python, Julia, R, Go and OCaml

In one of my recent posts I showed some F# code for storing and reading integers as binary data. Since then I've created different versions of the reading part in Python, Julia, Go, OCaml and R to be able to compare the performance of the different languages on this simple task and to get a feeling on what it's like to program in Julia, Go and OCaml as I hadn't created any programs in these languages yet.

Below table shows the benchmark results for reading 10 times 10000 values and 10000 times 10 values with links to the source code files in the different programming languages:

Language	10 times 10000 values	10000 times 10 values
F#	6 seconds	20 seconds
Python	26 seconds	40 seconds
Julia	45 seconds	72 seconds
Go	8 seconds	25 seconds
OCaml	2.5 seconds	48 seconds
R	110 seconds	NA

The overall fastest version is the one written in F# but note that it's also the version I have tweaked the most. As I'm not very experienced in most of the languages so any performance tips are welcome. Note that I tried using memory mapped files in .NET and Python this improved performance when querying lots of values from the same file but also made it worse in other cases.

The implementation of the functionality is most of the times rather similar in the different languages. Some notable differences where:

• Julia apparently doesn't have a null value so I refrained from checking whether the read integer value was equal to the int32 minimum value (-2147483648).
• In Go converting the bytes to integers was faster with a custom function.
• I didn't find a function in the OCaml Core library to convert bytes to a 32-bit integer, but luckily I found one on Stack Overflow.

F#:

open System
open System.IO

let readValue (reader:BinaryReader) cellIndex = 
    reader.BaseStream.Seek(int64 (cellIndex*4), SeekOrigin.Begin) |> ignore
    match reader.ReadInt32() with
    | Int32.MinValue -> None
    | v -> Some(v)
        
let readValues indices fileName = 
    use reader = new BinaryReader(File.Open(fileName, FileMode.Open, FileAccess.Read, FileShare.Read))
    let values = Array.map (readValue reader) indices
    values

Python:

def read_values(filename, indices):
    # indices are sorted and unique
    values = []
    with open(filename, 'rb') as f:
        for index in indices:
            f.seek(index*4L, os.SEEK_SET)
            b = f.read(4)
            v = struct.unpack("@i", b)[0]
            if v == -2147483648:
                v = None
            values.append(v)
    return values

Julia:

function readvalue(stream, position)
    seek(stream, position)
    return read(stream, Int32)
end

function readvalues(filename::String, indices)
    stream = open(filename, "r")
    try
        return Int32[readvalue(stream, index*4) for index in indices]
    finally
        close(stream)
    end
end

Go:

import ("os")
func bytes2int(b []byte) int32 {
    v := int32(0)
    for  i := 0; i < 4; i++ {
        v = v | (int32(b[i]) << (uint(8*i)))
    }
    return v
}

func readValues(indices []int, filename string) []int32 {
    results := make([]int32, len(indices))
    b := make([]byte, 4)
    f,_ := os.Open(filename)
    for i, cellIndex := range indices {
        f.Seek(int64(cellIndex*4), os.SEEK_SET)
        f.Read(b)
        value := bytes2int(b) // around 10-20% faster then binary.Read
        if value != -2147483648 {
            results[i] = value
        } else {
            results[i] = 99999
        }
    }
    return results
}

OCaml:

let input_le_int32 inchannel = (* http://stackoverflow.com/a/6031286/477367 *)
  let res = ref 0l in
    for i = 0 to 3 do
      let byte = input_byte inchannel in
        res := Int32.logor !res (Int32.shift_left (Int32.of_int byte) (8*i))
    done;

    match !res with
      | -2147483648l -> None
      | v -> Some(v)

let readvalue inchannel index =
  seek_in inchannel (index*4);
  input_le_int32 inchannel

let readvalues (indices:int array) filename =
  let inchannel = open_in_bin filename in
    try
      let result = Array.map (readvalue inchannel) indices in
        close_in inchannel;
        result
    with e ->
      close_in_noerr inchannel;
      raise e

R:

read.values <- function(filename, indices) {
  conn <- file(filename, "rb")
  read.value <- function(index) {
    seek(conn, where=index*4)
    readBin(conn, integer(), size = 4, n = 1, endian = "little")
  }
  r <- sapply(indices,read.value)
  close(conn)
  r[r==-2147483648] <- NA
  r
}

Any suggestions for improving the implementation in one of the above programming languages ? Which language would you like to compare my results with ? Which other language(s) do you expect to be faster than my benchmark results ? Can you help me out with a version in your favorite language or in C, fortran, Common Lisp, Scheme, Clojure, Java or J ?