Compute costs for reading and writing data.wip-ecrts14-pgm

1 files changed, 205 insertions, 0 deletions

diff --git a/distill_write_cold.py b/distill_write_cold.py
new file mode 100755
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+++ b/distill_write_cold.py
@@ -0,0 +1,205 @@
+#!/usr/bin/env python
+
+import os
+import re
+import fnmatch
+import shutil as sh
+import sys
+import csv
+import numpy as np
+from scipy.stats import scoreatpercentile
+import bisect
+from optparse import OptionParser
+
+from utils.machines import machines
+
+import utils.iqr
+
+class Topology:
+        ncpus, root, leaves, dist_mat = 0, None, None, None
+        levels = ['L1', 'L2', 'L3', 'Mem', 'System']
+
+        class Node:
+                idx, name, parent, children = 0, 'Unk', None, None
+                def __init__(self, idx, name, parent = None):
+                        self.idx = idx
+                        self.name = name
+                        self.parent = parent
+                        self.children = []
+                def __repr__(self):
+                        return self.name + '_' + str(self.idx)
+
+        def __build_level_above(self, machine, l, child_nodes):
+                key = 'n' + l
+                if key in machine:
+                        cluster_sz = machine[key]
+                else:
+                        cluster_sz = 1
+                nchildren = len(child_nodes)
+                nodes = [self.Node(idx, l) for idx in range(nchildren/cluster_sz)]
+                for i in range(len(child_nodes)):
+                        child_nodes[i].parent = nodes[i/cluster_sz]
+                        nodes[i/cluster_sz].children.append(child_nodes[i])
+                return nodes
+
+        def __find_dist(self, a, b):
+                if a != b:
+                        # pass-through (ex. as CPU is to private L1)
+                        if len(a.parent.children) == 1:
+                                return self.__find_dist(a.parent, b.parent)
+                        else:
+                                return 1 + self.__find_dist(a.parent, b.parent)
+                return 0
+
+        def __build_dist_matrix(self):
+                dist_mat = np.empty([self.ncpus, self.ncpus], int)
+                for i in range(self.ncpus):
+                        for j in range(i, self.ncpus):
+                                dist_mat[i,j] = dist_mat[j,i] = self.__find_dist(self.leaves[i], self.leaves[j])
+                return dist_mat
+
+        def __init__(self, machine):
+                self.ncpus = machine['sockets']*machine['cores_per_socket']
+
+                # build the Topology bottom up
+                self.leaves = [self.Node(idx, 'CPU') for idx in range(self.ncpus)]
+                nodes = self.leaves
+                for l in self.levels:
+                        nodes = self.__build_level_above(machine, l, nodes)
+                self.root = nodes
+
+                self.dist_mat = self.__build_dist_matrix()
+
+
+        def __repr_level(self, node, stem, buf):
+                spacing = 3
+                buf += stem + node.name + '_' + str(node.idx) + '\n'
+                for c in node.children:
+                        buf = self.__repr_level(c, stem + ' '*spacing, buf)
+                return buf
+
+        def __repr__(self):
+                buf = self.__repr_level(self.root[0], '', '')
+                return buf
+
+        def distance(self, a, b):
+                return self.dist_mat[a,b]
+
+
+topologies = {}
+def get_topo(host):
+        if host in topologies:
+                return topologies[host]
+        else:
+                topo = Topology(machines[host])
+                topologies[host] = topo
+                return topo
+
+def non_polluter_filename(csv_file):
+        return re.sub(r"polluters=True", r"polluters=False", csv_file)
+
+# find the max/avg/std of preemption and migration
+def process_cpmd(csv_file, params):
+
+        if 'pco' not in params:
+                raise Exception(('not producer/consumer overhead file: %s)') % csv_file)
+
+        topo = get_topo(params['host'])
+
+        print 'processing ' + csv_file
+
+        ifile = open(csv_file, "r")
+        bestcase = open(non_polluter_filename(csv_file), "r")
+
+        reader = csv.reader(ifile)
+        bc_reader = csv.reader(bestcase)
+        costs = {}
+
+        SAMPLE = 0
+        WSS = 1
+        DELAY = 2
+        LAST_CPU = 3
+        NEXT_CPU = 4
+        DIST = 5
+        PRODUCE_COLD = 6
+        PRODUCE_HOT = 7
+        CONSUME_COLD = 8
+        CONSUME_HOT = 9
+
+        for (row, bc_row) in zip(reader, bc_reader):
+                cold = int(row[PRODUCE_COLD])
+                distance = int(row[DIST])
+                if distance not in costs:
+                        costs[distance] = []
+                costs[distance].append(cold)
+
+        for d,c in costs.iteritems():
+                arr = np.array(c, float)
+                arr = np.sort(arr)
+                (arr, mincut, maxcut) = utils.iqr.apply_iqr(arr, 1.5)
+                for x in np.nditer(arr, op_flags=['readwrite']):
+                        x[...] = utils.machines.cycles_to_us(params['host'], x)
+                costs[d] = arr
+
+        stats = {}
+#       print costs
+        for d,arr in costs.iteritems():
+                stats[d] = {'max':arr.max(), 'median':np.median(arr), 'mean':arr.mean(), 'std':arr.std()}
+
+        return stats
+
+def parse_args():
+        parser = OptionParser("usage: %prog [files...]")
+        return parser.parse_args()
+
+def safe_split(t, delim):
+        t = t.split(delim)
+        if len(t) == 1:
+                t = tuple([t[0], None])
+        return t
+
+def get_level(machine, ncpus):
+        dist = get_topo(machine).distance(0, int(ncpus)-1)
+        names = ['L1', 'L2', 'L3', 'mem', 'sys']
+        if dist <= len(names):
+                return names[dist]
+        else:
+                raise Exception("Unable to determine level.")
+        return ''
+
+def main():
+        opts, args = parse_args()
+
+        files = filter(os.path.exists, args)
+
+        regex = fnmatch.translate("pco_*.csv")
+        csvs = re.compile(regex)
+        files = filter(csvs.search, files)
+
+        results = {}
+        for f in files:
+                temp = os.path.basename(f).split(".csv")[0]
+                tokens = temp.split("_")
+
+                params = {k:v for (k,v) in map(lambda x: safe_split(x, "="), tokens)}
+                common = tuple([params['host'], params['ncpu'], params['polluters'], params['walk'], params['hpages'], params['upages']])
+                if common not in results:
+                        results[common] = {}
+                results[common][int(params['wss'])] = process_cpmd(f, params)
+
+#       print results
+        for common in results:
+                trends = results[common]
+                for t in ['max', 'median', 'mean']:
+                        name = 'dwo_cold_host=%s_lvl=%s_polluters=%s_walk=%s_hpages=%s_upages=%s_type=%s.csv' % (common[0], get_level(common[0], common[1]), common[2], common[3], common[4], common[5], t)
+                        f = open(name, 'w')
+                        f.write('WSS,L1,L2,L3,MEM\n')
+                        for w,stats in iter(sorted(trends.iteritems())):
+                                f.write('%d' % w)
+                                for i,data in iter(sorted(stats.iteritems())):
+                                        val = data[t]
+                                        f.write(',%.6f' % val)
+                                f.write('\n')
+
+if __name__ == '__main__':
+        main()