Adding gem5 source to svn.
git-svn-id: https://www4.informatik.uni-erlangen.de/i4svn/danceos/trunk/devel/fail@1819 8c4709b5-6ec9-48aa-a5cd-a96041d1645a
This commit is contained in:
friemel
385
simulators/gem5/util/stats/dbinit.py
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385
simulators/gem5/util/stats/dbinit.py
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# Copyright (c) 2003-2004 The Regents of The University of Michigan
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met: redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer;
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# redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution;
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# neither the name of the copyright holders nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# Authors: Nathan Binkert
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import MySQLdb
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class MyDB(object):
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def __init__(self, options):
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self.name = options.db
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self.host = options.host
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self.user = options.user
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self.passwd = options.passwd
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self.mydb = None
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self.cursor = None
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def admin(self):
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self.close()
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self.mydb = MySQLdb.connect(db='mysql', host=self.host, user=self.user,
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passwd=self.passwd)
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self.cursor = self.mydb.cursor()
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def connect(self):
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self.close()
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self.mydb = MySQLdb.connect(db=self.name, host=self.host,
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user=self.user, passwd=self.passwd)
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self.cursor = self.mydb.cursor()
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def close(self):
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if self.mydb is not None:
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self.mydb.close()
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self.cursor = None
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def query(self, sql):
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self.cursor.execute(sql)
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def drop(self):
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self.query('DROP DATABASE IF EXISTS %s' % self.name)
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def create(self):
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self.query('CREATE DATABASE %s' % self.name)
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def populate(self):
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#
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# Each run (or simulation) gets its own entry in the runs table to
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# group stats by where they were generated
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#
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# COLUMNS:
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# 'id' is a unique identifier for each run to be used in other
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# tables.
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# 'name' is the user designated name for the data generated. It is
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# configured in the simulator.
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# 'user' identifies the user that generated the data for the given
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# run.
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# 'project' another name to identify runs for a specific goal
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# 'date' is a timestamp for when the data was generated. It can be
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# used to easily expire data that was generated in the past.
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# 'expire' is a timestamp for when the data should be removed from
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# the database so we don't have years worth of junk.
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#
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# INDEXES:
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# 'run' is indexed so you can find out details of a run if the run
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# was retreived from the data table.
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# 'name' is indexed so that two all run names are forced to be unique
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#
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self.query('''
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CREATE TABLE runs(
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rn_id SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT,
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rn_name VARCHAR(200) NOT NULL,
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rn_sample VARCHAR(32) NOT NULL,
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rn_user VARCHAR(32) NOT NULL,
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rn_project VARCHAR(100) NOT NULL,
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rn_date TIMESTAMP NOT NULL,
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rn_expire TIMESTAMP NOT NULL,
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PRIMARY KEY (rn_id),
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UNIQUE (rn_name,rn_sample)
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) TYPE=InnoDB''')
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#
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# The stat table gives us all of the data for a particular stat.
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#
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# COLUMNS:
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# 'stat' is a unique identifier for each stat to be used in other
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# tables for references.
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# 'name' is simply the simulator derived name for a given
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# statistic.
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# 'descr' is the description of the statistic and what it tells
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# you.
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# 'type' defines what the stat tells you. Types are:
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# SCALAR: A simple scalar statistic that holds one value
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# VECTOR: An array of statistic values. Such a something that
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# is generated per-thread. Vectors exist to give averages,
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# pdfs, cdfs, means, standard deviations, etc across the
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# stat values.
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# DIST: Is a distribution of data. When the statistic value is
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# sampled, its value is counted in a particular bucket.
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# Useful for keeping track of utilization of a resource.
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# (e.g. fraction of time it is 25% used vs. 50% vs. 100%)
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# VECTORDIST: Can be used when the distribution needs to be
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# factored out into a per-thread distribution of data for
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# example. It can still be summed across threads to find
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# the total distribution.
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# VECTOR2D: Can be used when you have a stat that is not only
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# per-thread, but it is per-something else. Like
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# per-message type.
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# FORMULA: This statistic is a formula, and its data must be
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# looked up in the formula table, for indicating how to
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# present its values.
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# 'subdata' is potentially used by any of the vector types to
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# give a specific name to all of the data elements within a
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# stat.
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# 'print' indicates whether this stat should be printed ever.
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# (Unnamed stats don't usually get printed)
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# 'prereq' only print the stat if the prereq is not zero.
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# 'prec' number of decimal places to print
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# 'nozero' don't print zero values
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# 'nonan' don't print NaN values
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# 'total' for vector type stats, print the total.
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# 'pdf' for vector type stats, print the pdf.
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# 'cdf' for vector type stats, print the cdf.
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#
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# The Following are for dist type stats:
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# 'min' is the minimum bucket value. Anything less is an underflow.
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# 'max' is the maximum bucket value. Anything more is an overflow.
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# 'bktsize' is the approximate number of entries in each bucket.
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# 'size' is the number of buckets. equal to (min/max)/bktsize.
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#
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# INDEXES:
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# 'stat' is indexed so that you can find out details about a stat
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# if the stat id was retrieved from the data table.
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# 'name' is indexed so that you can simply look up data about a
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# named stat.
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#
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self.query('''
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CREATE TABLE stats(
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st_id SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT,
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st_name VARCHAR(255) NOT NULL,
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st_descr TEXT NOT NULL,
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st_type ENUM("SCALAR", "VECTOR", "DIST", "VECTORDIST",
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"VECTOR2D", "FORMULA") NOT NULL,
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st_print BOOL NOT NULL,
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st_prereq SMALLINT UNSIGNED NOT NULL,
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st_prec TINYINT NOT NULL,
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st_nozero BOOL NOT NULL,
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st_nonan BOOL NOT NULL,
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st_total BOOL NOT NULL,
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st_pdf BOOL NOT NULL,
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st_cdf BOOL NOT NULL,
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st_min DOUBLE NOT NULL,
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st_max DOUBLE NOT NULL,
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st_bktsize DOUBLE NOT NULL,
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st_size SMALLINT UNSIGNED NOT NULL,
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PRIMARY KEY (st_id),
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UNIQUE (st_name)
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) TYPE=InnoDB''')
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#
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# This is the main table of data for stats.
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#
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# COLUMNS:
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# 'stat' refers to the stat field given in the stat table.
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#
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# 'x' referrs to the first dimension of a multi-dimensional stat. For
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# a vector, x will start at 0 and increase for each vector
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# element.
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# For a distribution:
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# -1: sum (for calculating standard deviation)
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# -2: sum of squares (for calculating standard deviation)
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# -3: total number of samples taken (for calculating
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# standard deviation)
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# -4: minimum value
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# -5: maximum value
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# -6: underflow
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# -7: overflow
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# 'y' is used by a VECTORDIST and the VECTOR2D to describe the second
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# dimension.
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# 'run' is the run that the data was generated from. Details up in
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# the run table
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# 'tick' is a timestamp generated by the simulator.
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# 'data' is the actual stat value.
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#
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# INDEXES:
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# 'stat' is indexed so that a user can find all of the data for a
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# particular stat. It is not unique, because that specific stat
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# can be found in many runs and samples, in addition to
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# having entries for the mulidimensional cases.
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# 'run' is indexed to allow a user to remove all of the data for a
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# particular execution run. It can also be used to allow the
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# user to print out all of the data for a given run.
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#
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self.query('''
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CREATE TABLE data(
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dt_stat SMALLINT UNSIGNED NOT NULL,
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dt_x SMALLINT NOT NULL,
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dt_y SMALLINT NOT NULL,
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dt_run SMALLINT UNSIGNED NOT NULL,
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dt_tick BIGINT UNSIGNED NOT NULL,
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dt_data DOUBLE NOT NULL,
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INDEX (dt_stat),
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INDEX (dt_run),
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UNIQUE (dt_stat,dt_x,dt_y,dt_run,dt_tick)
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) TYPE=InnoDB;''')
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#
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# Names and descriptions for multi-dimensional stats (vectors, etc.)
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# are stored here instead of having their own entry in the statistics
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# table. This allows all parts of a single stat to easily share a
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# single id.
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#
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# COLUMNS:
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# 'stat' is the unique stat identifier from the stat table.
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# 'x' is the first dimension for multi-dimensional stats
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# corresponding to the data table above.
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# 'y' is the second dimension for multi-dimensional stats
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# corresponding to the data table above.
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# 'name' is the specific subname for the unique stat,x,y combination.
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# 'descr' is the specific description for the uniqe stat,x,y
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# combination.
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#
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# INDEXES:
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# 'stat' is indexed so you can get the subdata for a specific stat.
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#
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self.query('''
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CREATE TABLE subdata(
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sd_stat SMALLINT UNSIGNED NOT NULL,
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sd_x SMALLINT NOT NULL,
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sd_y SMALLINT NOT NULL,
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sd_name VARCHAR(255) NOT NULL,
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sd_descr TEXT,
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UNIQUE (sd_stat,sd_x,sd_y)
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) TYPE=InnoDB''')
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#
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# The formula table is maintained separately from the data table
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# because formula data, unlike other stat data cannot be represented
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# there.
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#
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# COLUMNS:
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# 'stat' refers to the stat field generated in the stat table.
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# 'formula' is the actual string representation of the formula
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# itself.
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#
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# INDEXES:
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# 'stat' is indexed so that you can just look up a formula.
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#
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self.query('''
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CREATE TABLE formulas(
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fm_stat SMALLINT UNSIGNED NOT NULL,
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fm_formula BLOB NOT NULL,
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PRIMARY KEY(fm_stat)
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) TYPE=InnoDB''')
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#
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# Each stat used in each formula is kept in this table. This way, if
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# you want to print out a particular formula, you can simply find out
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# which stats you need by looking in this table. Additionally, when
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# you remove a stat from the stats table and data table, you remove
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# any references to the formula in this table. When a formula is no
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# longer referred to, you remove its entry.
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#
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# COLUMNS:
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# 'stat' is the stat id from the stat table above.
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# 'child' is the stat id of a stat that is used for this formula.
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# There may be many children for any given 'stat' (formula)
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#
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# INDEXES:
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# 'stat' is indexed so you can look up all of the children for a
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# particular stat.
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# 'child' is indexed so that you can remove an entry when a stat is
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# removed.
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#
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self.query('''
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CREATE TABLE formula_ref(
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fr_stat SMALLINT UNSIGNED NOT NULL,
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fr_run SMALLINT UNSIGNED NOT NULL,
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UNIQUE (fr_stat,fr_run),
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INDEX (fr_stat),
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INDEX (fr_run)
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) TYPE=InnoDB''')
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# COLUMNS:
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# 'event' is the unique event id from the event_desc table
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# 'run' is simulation run id that this event took place in
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# 'tick' is the tick when the event happened
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#
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# INDEXES:
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# 'event' is indexed so you can look up all occurences of a
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# specific event
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# 'run' is indexed so you can find all events in a run
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# 'tick' is indexed because we want the unique thing anyway
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# 'event,run,tick' is unique combination
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self.query('''
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CREATE TABLE events(
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ev_event SMALLINT UNSIGNED NOT NULL,
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ev_run SMALLINT UNSIGNED NOT NULL,
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ev_tick BIGINT UNSIGNED NOT NULL,
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INDEX(ev_event),
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INDEX(ev_run),
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INDEX(ev_tick),
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UNIQUE(ev_event,ev_run,ev_tick)
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) TYPE=InnoDB''')
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# COLUMNS:
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# 'id' is the unique description id
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# 'name' is the name of the event that occurred
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#
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# INDEXES:
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# 'id' is indexed because it is the primary key and is what you use
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# to look up the descriptions
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# 'name' is indexed so one can find the event based on name
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#
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self.query('''
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CREATE TABLE event_names(
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en_id SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT,
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en_name VARCHAR(255) NOT NULL,
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PRIMARY KEY (en_id),
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UNIQUE (en_name)
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) TYPE=InnoDB''')
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def clean(self):
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self.query('''
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DELETE data
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FROM data
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LEFT JOIN runs ON dt_run=rn_id
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WHERE rn_id IS NULL''')
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self.query('''
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DELETE formula_ref
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FROM formula_ref
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LEFT JOIN runs ON fr_run=rn_id
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WHERE rn_id IS NULL''')
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self.query('''
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DELETE formulas
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FROM formulas
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LEFT JOIN formula_ref ON fm_stat=fr_stat
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WHERE fr_stat IS NULL''')
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self.query('''
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DELETE stats
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FROM stats
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LEFT JOIN data ON st_id=dt_stat
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WHERE dt_stat IS NULL''')
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self.query('''
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DELETE subdata
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FROM subdata
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LEFT JOIN data ON sd_stat=dt_stat
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WHERE dt_stat IS NULL''')
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self.query('''
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DELETE events
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FROM events
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LEFT JOIN runs ON ev_run=rn_id
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WHERE rn_id IS NULL''')
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self.query('''
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DELETE event_names
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FROM event_names
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LEFT JOIN events ON en_id=ev_event
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WHERE ev_event IS NULL''')
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Reference in New Issue
Block a user