pstoll

// http://tour.golang.org/#45
// by Perry Stoll

package main
 
import (
        "fmt"
        "math"
)
 

pstoll

package main

// http://tour.golang.org/#46
// question - is this efficient?
// ie is range operator returning refs or copies of the string elements?

import (
        "tour/wc"
        "strings"
)

pstoll

// http://tour.golang.org/#48
// 
package main
 
import "fmt"
 
// fibonacci is a function that returns
// a function that returns an int.
func fibonacci() func() int {
        prev := 0;

pstoll

package main

import "fmt"
import "math/cmplx"

func Cbrt(x complex128) complex128 {
	z:=complex128(1.0)
        min_delta := float64(0.00000000001)
        delta := complex128(1.0)
        var iter int

pstoll

#! /usr/bin/env escript
%%! -hidden -noshell -noinput


-mode(compile).


-export([
    init_shell_log/1
]).

pstoll

--- src/http/ngx_http_variables.c.orig  2010-01-11 03:21:46.000000000 -0800
+++ src/http/ngx_http_variables.c       2010-02-18 10:01:32.000000000 -0800
@@ -93,6 +93,9 @@
 static ngx_int_t ngx_http_variable_pid(ngx_http_request_t *r,
     ngx_http_variable_value_t *v, uintptr_t data);
 
+static ngx_int_t ngx_http_variable_start_time(ngx_http_request_t *r,
+    ngx_http_variable_value_t *v, uintptr_t data);
+
 /*

pstoll

diff --git a/setup.py b/setup.py
index b8b003b..e8bf426 100644
--- a/setup.py
+++ b/setup.py
@@ -1,9 +1,16 @@
 from setuptools import setup, Extension
 from glob import glob
+import os
+
+os.environ["CC"] = "/usr/local/bin/clang-omp -fopenmp"

pstoll

pstoll$ python test.py
test1  arrays: 6.956779105006717
test1a arrays: 6.8869501760054845
test2  native vectors: 59.97818154899869
test2a native vectors: 90.65342697499727

pstoll

python test.py
test1arr for 100000000 primes: write directly to array with new generate_n_primes_array 2.82 sec
test1iter for 100000000 primes: write to numpy data using iter interface: 3.65 sec
test1iter2 for 100000000 primes: write to numpy data using iter interface data access: 3.40 sec
test1np for 100000000 primes: use normal vector generation but copy directly to numpy on output: 4.16 sec
test2 for 100000000 primes: current vector generate_n_primes with numpy copy from vector to output list: 21.03 sec
test2a for 100000000 primes: current vector, copy to output list, create numpy array: 36.25 sec

pstoll

# 10B values using new prime generation interface directly to numpy arrays
#  total time - 1m:43sec for the entire calculation.
#  prime generation was only 10 sec. 
#  it all fit in RAM for my 16GB machine.
prime-hexagon pstoll$ python ./primespin.py --nvalues=$(echo 10,000,000,000|tr -d ,) --chunks 10 --skip 1000000
2015-11-18 02:08:34,722 - prime_hex - INFO - starting generating primes from 1 to 10000000000
trying direct numpy prime generation...
2015-11-18 02:08:44,534 - prime_hex - INFO - done generating primes
2015-11-18 02:08:44,534 - prime_hex - INFO - compute_spins: generating mod6Values
2015-11-18 02:09:04,147 - prime_hex - INFO - compute_spins: done mod6Values