problem with parallel foreach
Rikki Cattermole via Digitalmars-d-learn
digitalmars-d-learn at puremagic.com
Wed May 13 04:16:17 PDT 2015
On 13/05/2015 2:59 a.m., Gerald Jansen wrote:
> I am a data analyst trying to learn enough D to decide whether to use D
> for a new project rather than Python + Fortran. I have recoded a
> non-trivial Python program to do some simple parallel data processing
> (using the map function in Python's multiprocessing module and parallel
> foreach in D). I was very happy that my D version ran considerably
> faster that Python version when running a single job but was soon
> dismayed to find that the performance of my D version deteriorates
> rapidly beyond a handful of jobs whereas the time for the Python version
> increases linearly with the number of jobs per cpu core.
>
> The server has 4 quad-core Xeons and abundant memory compared to my
> needs for this task even though there are several million records in
> each dataset. The basic structure of the D program is:
>
> import std.parallelism; // and other modules
> function main()
> {
> // ...
> // read common data and store in arrays
> // ...
> foreach (job; parallel(jobs, 1)) {
> runJob(job, arr1, arr2.dup);
> }
> }
> function runJob(string job, in int[] arr1, int[] arr2)
> {
> // read file of job specific data file and modify arr2 copy
> // write job specific output data file
> }
>
> The output of /usr/bin/time is as follows:
>
> Lang Jobs User System Elapsed %CPU
> Py 1 45.17 1.44 0:46.65 99
> D 1 8.44 1.17 0:09.24 104
>
> Py 2 79.24 2.16 0:48.90 166
> D 2 19.41 10.14 0:17.96 164
>
> Py 30 1255.17 58.38 2:39.54 823 * Pool(12)
> D 30 421.61 4565.97 6:33.73 1241
>
> (Note that the Python program was somewhat optimized with numpy
> vectorization and a bit of numba jit compilation.)
>
> The system time varies widely between repititions for D with multiple
> jobs (eg. from 3.8 to 21.5 seconds for 2 jobs).
>
> Clearly simple my approach with parallel foreach has some problem(s).
> Any suggestions?
>
> Gerald Jansen
I managed to rewrite most of the core IO part to use ranges.
However runTrait I could not rewrite just by reading it. To do so, you
would focus on the line being read instead of the trait.
There are many lines of input data. Very few traits.
There is a couple more places where IO is being performed in runTrait.
Again turn them into ranges like I've done.
But more importantly, split up the functions that performs the logic in
runTrait as much as possible. Small pieces of code can be optimized
easier then large blobs.
Anyway that is my attempt at getting it faster.
/**
Assign unknown parent groups (UPG) - trait specific version
UPG defined in 5-year intervals for sires and dams of animals
with a domestic or foreign animal ID (eg. IT vs XX).
*/
// History:
// 2015.05.12 GJ - original version in D
/*import std.stdio, std.path, std.conv, std.string, std.datetime;
import std.algorithm, std.parallelism;
import std.math: ceil;
import core.memory : GC;*/
struct ControlVars // TODO: read this from a control file
{
import std.stdio : File;
File log;
string osmdata = ".";
int ped_cycles = 2;
int ped_cc_start = 3;
string ped_country = "IT";
int ped_cutoff = 1980;
int ped_upg_size = 50;
bool dereg_bulls_only = false;
}
//------------------------------------------------------------------------------;
void main() {
/**
pedupg main - store pednum.csv and process traits in parallel;
*/
import std.stdio : stdout, File;
import std.path : buildPath;
import std.parallelism : parallel;
import core.memory : GC;
ControlVars g = ControlVars(/*stdout*/File("out.txt", "w"));
GC.disable;
GC.reserve(1024 * 1024 * 1024);
datedMessage("pedupg started ...", g.log);
auto suffix = "";
auto logfile = buildPath(g.osmdata, "log", "pedupg" ~ suffix ~ ".log");
int nPed = 1 + getPednumMax(g.osmdata);
g.log.writefln("%8d nPed (i.e. pednum_max + 1)", nPed);
// read and store pedigree info
FileReader fileReader = FileReader(buildPath(g.osmdata, "wrk",
"pednum.csv"), g);
// TODO: readd
//log.writefln("%8d records read from %s", j, pednum);
auto traits = getRunTraits(g);
foreach(fl; fileReader.parallel) {
import std.stdio : writeln;
import std.conv : text;
g.log.write(text(fl) ~ "\n");
}
datedMessage("pedupg all done.", g.log);
}
struct FileLine {
int sire;
int dam;
short byear;
bool ccode;
}
struct FileReader {
private {
import std.stdio : File;
import std.traits : ReturnType;
ControlVars g;
FileLine last;
bool done;
ReturnType!(File.byLine!()) fileReader;
}
this(string filename, ControlVars g) {
this.g = g;
fileReader = File(filename, "r").byLine;
popFront;
}
@property {
FileLine front() {
return last;
}
bool empty() {
return done;
}
}
void popFront() {
import std.conv : to;
import std.string : split;
assert(!done);
auto rec = split(fileReader.front, ',');
auto col0 = g.ped_cc_start - 1;
last.sire = to!int(rec[1]);
last.dam = to!int(rec[2]);
last.byear = to!short(rec[4][0 .. 4]);
// rec[7] is animID
last.ccode = (rec[7][col0 .. col0 + 2] == g.ped_country) ? 0 : 1;
fileReader.popFront;
done = fileReader.empty;
}
}
/*void runTrait(in string trt, in string suffix, in int nPed, int[] sire,
int[] dam, in short[] byear, in byte[] ccode)
{
auto logfile = g.osmdata~"/log/"~trt~"/pedupg"~suffix~".log";
auto log = File(logfile, "w");
datedMessage("pedupg started for trait " ~ trt, log);
// set up initial counts
int minYear = g.ped_cutoff;
int maxYear = 2015; // date.today().year;
// read and mark animals with EBV (generation 1);
byte[] gen; gen.length = nPed; gen[0] = -1;
auto ebvfile = g.osmdata~"/wrk/"~trt~"/ebv"~suffix~".csv";
auto n = 0, n1 = 0;
foreach (line; File(ebvfile, "r").byLine()) {
n++;
auto rec = split(chomp(line), ","); // ja,ebv,rel,ori
if (rec[3] < "3" || !g.dereg_bulls_only) {
auto ja = to!int(rec[0]);
if (!gen[ja]) {
gen[to!int(rec[0])] = 1;
n1++;
}
}
}
log.writef("%8d lines read from %s\n", n, ebvfile);
log.writef("%8d bulls%s with initial rel>0\n", n1,
(g.dereg_bulls_only) ? "" : " and cows");
// mark ancestor generations (2 .. ped_cycles+1)) {
int js, jd, m;
foreach (i; 1 .. g.ped_cycles + 1) {
m = 0;
foreach (ja; 1 .. nPed) {
if (gen[ja] != i) continue;
js = sire[ja];
if (js > 0 && !gen[js]) {
gen[js] = cast(byte)(i + 1);
m++;
}
jd = dam[ja];
if (jd > 0 && !gen[jd]) {
gen[jd] = cast(byte)(i + 1);
m++;
}
}
log.writef("%8d parents added in generation %d\n", m, i);
n += m;
}
log.writef("%8d animals in initial pedigree\n", nPed);
log.writef("%8d animals connected to EBV for trait %s\n\n", n, trt);
// count unknown parents of marked animals by 5-year intervals
int k1, kMax = 1 + (maxYear + 4 - minYear) / 5; // max year group
int[][][] counts = new int[][][](2, 2, kMax);
foreach (ja; 1 .. nPed) {
if (gen[ja] == 0) continue;
// set parents of final generation to unknown
if (gen[ja] == g.ped_cycles + 1) {
sire[ja] = 0;
dam[ja] = 0;
}
if (!sire[ja]) {
k1 = yearGroup(byear[ja], minYear, maxYear);
counts[ccode[ja]][0][k1]++;
}
if (!dam[ja]) {
// -- offset byear by 2 for dam
k1 = yearGroup(byear[ja] + 2, minYear, maxYear);
if (ccode[ja]>1 || k1 >= kMax) {
writeln("ja=",ja," cc=",ccode[ja]," byear=",byear[ja],"
k1=",k1);
return;//(99999);
}
counts[ccode[ja]][1][k1]++;
}
}
auto CC = g.ped_country;
log.writef("starting group counts for trait %s\n", trt);
log.writef("years %ssire | years %sdam", CC, CC);
log.writef(" | years XXsire | years XXdam\n");
foreach (k; 0 .. kMax) {
auto years1 = format("%d-%d", (!k) ? 1900 : minYear+5*k-4,
minYear+5*k);
auto years2 = format("%d-%d", (!k) ? 1900 : minYear+5*k-6,
(k == kMax - 1) ? maxYear : minYear+5*k-2);
log.writef("%s %7d | %s %7d | %s %7d | %s %7d\n",
years1, counts[0][0][k], years2, counts[0][1][k],
years1, counts[1][0][k], years2, counts[1][1][k]);
}
// pool small groups and assign final groups;
int[][][] group = new int[][][](2, 2, kMax);
struct UpgInfo
{
int count, k1, k2, cc, sx;
}
UpgInfo[] info;
UpgInfo u;
int upg = (-1);
foreach (cc; 0 .. 2) { // eg cc: 1="IT", 2="XX")
foreach (sx; 0 .. 2) { // 0=sire, 1=dam
// start a new group for each cc-sex combination;
upg++;
u.count = 0; u.k1 = 0; u.cc = cc; u.sx = sx;
auto remaining = reduce!"a+b"(counts[cc][sx]);
foreach (k; 0 .. kMax) { // 5-year byear groups
//writefln("cc=%s sx=%s k=%s upg=%s
rem=%s",cc,sx,k,upg,remaining);
if (u.count >= g.ped_upg_size && remaining >=
g.ped_upg_size) {
// okay to start a new group
// -- first save info on prevous group
info ~= u;
upg++;
u.count = 0; u.k1 = k; u.cc = cc; u.sx = sx;
}
group[cc][sx][k] = upg;
u.count += counts[cc][sx][k];
remaining -= counts[cc][sx][k];
u.k2 = k;
}
info ~= u; // save info on last group per cc*sx
}
}
auto nUpg = upg + 1;
log.writef("\nfinal group definitions and counts for trait %s\n", trt);
log.writef("| years %ssire UPG | years %sdam", CC, CC);
log.writef(" UPG | years XXsire UPG | years XXdam UPG |\n");
foreach (k; 0 .. kMax) {
auto years1 = format("%d-%d", (!k) ? 1900 : minYear+5*k-4,
minYear+5*k);
auto years2 = format("%d-%d", (!k) ? 1900 : minYear+5*k-6,
(k == kMax - 1) ? maxYear : minYear+5*k-2);
int g11 = group[0][0][k], g12 = group[0][1][k],
g21 = group[1][0][k], g22 = group[1][1][k];
log.writef("| %s %7d %3d | %s %7d %3d | %s %7d %3d | %s %7d %3d
|\n",
years1, info[g11].count, g11 + 1,
years2, info[g12].count, g12 + 1,
years1, info[g21].count, g21 + 1,
years2, info[g22].count, g22 + 1);
}
// write UPG definitions to upgdef.csv file
auto upglist = g.osmdata~"/wrk/"~trt~"/upglist"~suffix~".csv";
auto f = File(upglist, "w");
foreach (i; 0 .. nUpg) {
u = info[i];
auto yMin = (!u.k1) ? 1900 : minYear + 5*u.k1 - 2*(u.sx+2);
auto yMax = (u.k2 == kMax-1) ? maxYear : minYear + 5*u.k2 -
2*(u.sx);
f.writef("%d,%s%d,%d,%d,%d\n", -(i+1), (u.cc) ? "XX":
g.ped_country,
u.sx + 1, yMin, yMax, u.count);
}
f.close;
log.writef("\n%8d records written to %s\n", nUpg, upglist);
log.flush;
// re-write pednum.csv to pedupg.csv with UPG;
auto pednum = g.osmdata~"/wrk/pednum"~suffix~".csv";
auto pedupg = g.osmdata~"/wrk/"~trt~"/pedupg"~suffix~".csv";
f = File(pedupg, "w");
int ja = 0;
foreach (line; File(pednum, "r").byLine()) {
ja++;
if (sire[ja] && dam[ja]) { // neither sire nor dam unknown
f.write(line);
continue;
}
auto r = split(line, ',');
js = (sire[ja]) ? sire[ja] :
-(group[ccode[ja]][0][yearGroup(byear[ja], minYear,
maxYear)] + 1);
jd = (dam[ja]) ? dam[ja] :
-(group[ccode[ja]][1][yearGroup(byear[ja] + 2, minYear,
maxYear)] + 1);
f.write("%d,%d,%d,%s,%s,%s,%s,%s,%s", ja, js, jd, r[3], r[4], r[5],
r[6], r[7], r[8]);
}
log.writef("%8d records written to %s\n", nPed, pedupg);
datedMessage("pedupg done.", log);
return;// (nUpg);
}*/
int yearGroup(in int year, const int minYear, const int maxYear) @safe
pure {
/** Compute five year group code: eg. 0=1900-1980, 1=1981..1985, etc.
*/
if (year <= minYear) return(0);
else if (year >= maxYear) return((maxYear + 4 - minYear) / 5);
else return((year + 4 - minYear) / 5);
}
// some utility functions
void datedMessage(string msg, typeof(ControlVars.log) log) @system {
import std.stdio : writeln;
import std.datetime : Clock;
try {
log.writeln(Clock.currTime.toSimpleString()," ", msg);
} catch (Exception e){
writeln("ERROR: datedMessage");
}
}
int getPednumMax(string osmdata) @system {
import std.file : readText;
import std.path : buildPath;
import std.string : strip;
import std.conv : to;
return
buildPath(osmdata, "wrk", "pednum_max")
.readText
.strip
.to!int;
}
string[] getRunTraits(ControlVars g) @system {
import std.file : readText;
import std.path : buildPath;
import std.string : strip, split;
return
buildPath(g.osmdata, "wrk", "run_traits")
.readText
.strip
.split;
}
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