info.py

来自「M5,一个功能强大的多处理器系统模拟器.很多针对处理器架构,性能的研究都使用它作」· Python 代码 · 共 768 行 · 第 1/2 页

class VectorStat(Statistic,Vector):
    def __value__(self, run, item):
        if run not in self.data:
            return None
        return self.data[run][item][0]

    def __len__(self):
        return self.x

    def display(self, run=None):
        import display
        d = display.VectorDisplay()
        d.name = self.name
        d.desc = self.desc
        d.value = [ value(self, run, i) for i in xrange(len(self)) ]
        d.flags = self.flags
        d.precision = self.precision
        d.display()

class Formula(Value):
    def __getattribute__(self, attr):
        if attr not in ( '__scalar__', '__vector__', '__value__', '__len__' ):
            return super(Formula, self).__getattribute__(attr)

        formula = re.sub(':', '__', self.formula)
        value = eval(formula, self.source.stattop)
        return getattr(value, attr)

    def __str__(self):
        return self.name

class SimpleDist(Statistic):
    def __init__(self, sums, squares, samples):
        self.sums = sums
        self.squares = squares
        self.samples = samples

    def display(self, name, desc, flags, precision):
        import display
        p = display.Print()
        p.flags = flags
        p.precision = precision

        if self.samples > 0:
            p.name = name + ".mean"
            p.value = self.sums / self.samples
            p.display()

            p.name = name + ".stdev"
            if self.samples > 1:
                var = (self.samples * self.squares - self.sums ** 2) \
                      / (self.samples * (self.samples - 1))
                if var >= 0:
                    p.value = math.sqrt(var)
                else:
                    p.value = 'NaN'
            else:
                p.value = 0.0
            p.display()

        p.name = name + ".samples"
        p.value = self.samples
        p.display()

    def comparable(self, other):
        return True

    def __eq__(self, other):
        return self.sums == other.sums and self.squares == other.squares and \
               self.samples == other.samples

    def __isub__(self, other):
        self.sums -= other.sums
        self.squares -= other.squares
        self.samples -= other.samples
        return self

    def __iadd__(self, other):
        self.sums += other.sums
        self.squares += other.squares
        self.samples += other.samples
        return self

    def __itruediv__(self, other):
        if not other:
            return self
        self.sums /= other
        self.squares /= other
        self.samples /= other
        return self

class FullDist(SimpleDist):
    def __init__(self, sums, squares, samples, minval, maxval,
                 under, vec, over, min, max, bsize, size):
        self.sums = sums
        self.squares = squares
        self.samples = samples
        self.minval = minval
        self.maxval = maxval
        self.under = under
        self.vec = vec
        self.over = over
        self.min = min
        self.max = max
        self.bsize = bsize
        self.size = size

    def display(self, name, desc, flags, precision):
        import display
        p = display.Print()
        p.flags = flags
        p.precision = precision

        p.name = name + '.min_val'
        p.value = self.minval
        p.display()

        p.name = name + '.max_val'
        p.value = self.maxval
        p.display()

        p.name = name + '.underflow'
        p.value = self.under
        p.display()

        i = self.min
        for val in self.vec[:-1]:
            p.name = name + '[%d:%d]' % (i, i + self.bsize - 1)
            p.value = val
            p.display()
            i += self.bsize

        p.name = name + '[%d:%d]' % (i, self.max)
        p.value = self.vec[-1]
        p.display()


        p.name = name + '.overflow'
        p.value = self.over
        p.display()

        SimpleDist.display(self, name, desc, flags, precision)

    def comparable(self, other):
        return self.min == other.min and self.max == other.max and \
               self.bsize == other.bsize and self.size == other.size

    def __eq__(self, other):
        return self.sums == other.sums and self.squares == other.squares and \
               self.samples == other.samples

    def __isub__(self, other):
        self.sums -= other.sums
        self.squares -= other.squares
        self.samples -= other.samples

        if other.samples:
            self.minval = min(self.minval, other.minval)
            self.maxval = max(self.maxval, other.maxval)
            self.under -= under
            self.vec = map(lambda x,y: x - y, self.vec, other.vec)
            self.over -= over
        return self

    def __iadd__(self, other):
        if not self.samples and other.samples:
            self = other
            return self

        self.sums += other.sums
        self.squares += other.squares
        self.samples += other.samples

        if other.samples:
            self.minval = min(self.minval, other.minval)
            self.maxval = max(self.maxval, other.maxval)
            self.under += other.under
            self.vec = map(lambda x,y: x + y, self.vec, other.vec)
            self.over += other.over
        return self

    def __itruediv__(self, other):
        if not other:
            return self
        self.sums /= other
        self.squares /= other
        self.samples /= other

        if self.samples:
            self.under /= other
            for i in xrange(len(self.vec)):
                self.vec[i] /= other
            self.over /= other
        return self

class Dist(Statistic):
    def display(self):
        import display
        if not display.all and not (self.flags & flags.printable):
            return

        self.dist.display(self.name, self.desc, self.flags, self.precision)

    def comparable(self, other):
        return self.name == other.name and \
               self.dist.compareable(other.dist)

    def __eq__(self, other):
        return self.dist == other.dist

    def __isub__(self, other):
        self.dist -= other.dist
        return self

    def __iadd__(self, other):
        self.dist += other.dist
        return self

    def __itruediv__(self, other):
        if not other:
            return self
        self.dist /= other
        return self

class VectorDist(Statistic):
    def display(self):
        import display
        if not display.all and not (self.flags & flags.printable):
            return

        if isinstance(self.dist, SimpleDist):
            return

        for dist,sn,sd,i in map(None, self.dist, self.subnames, self.subdescs,
                                range(len(self.dist))):
            if len(sn) > 0:
                name = '%s.%s' % (self.name, sn)
            else:
                name = '%s[%d]' % (self.name, i)

            if len(sd) > 0:
                desc = sd
            else:
                desc = self.desc

            dist.display(name, desc, self.flags, self.precision)

        if (self.flags & flags.total) or 1:
            if isinstance(self.dist[0], SimpleDist):
                disttotal = SimpleDist( \
                    reduce(sums, [d.sums for d in self.dist]),
                    reduce(sums, [d.squares for d in self.dist]),
                    reduce(sums, [d.samples for d in self.dist]))
            else:
                disttotal = FullDist( \
                    reduce(sums, [d.sums for d in self.dist]),
                    reduce(sums, [d.squares for d in self.dist]),
                    reduce(sums, [d.samples for d in self.dist]),
                    min([d.minval for d in self.dist]),
                    max([d.maxval for d in self.dist]),
                    reduce(sums, [d.under for d in self.dist]),
                    reduce(sums, [d.vec for d in self.dist]),
                    reduce(sums, [d.over for d in self.dist]),
                    dist[0].min,
                    dist[0].max,
                    dist[0].bsize,
                    dist[0].size)

            name = '%s.total' % (self.name)
            desc = self.desc
            disttotal.display(name, desc, self.flags, self.precision)

    def comparable(self, other):
        return self.name == other.name and \
               alltrue(map(lambda x, y : x.comparable(y),
                           self.dist,
                           other.dist))

    def __eq__(self, other):
        return alltrue(map(lambda x, y : x == y, self.dist, other.dist))

    def __isub__(self, other):
        if isinstance(self.dist, (list, tuple)) and \
               isinstance(other.dist, (list, tuple)):
            for sd,od in zip(self.dist, other.dist):
                sd -= od
        else:
            self.dist -= other.dist
        return self

    def __iadd__(self, other):
        if isinstance(self.dist, (list, tuple)) and \
               isinstance(other.dist, (list, tuple)):
            for sd,od in zip(self.dist, other.dist):
                sd += od
        else:
            self.dist += other.dist
        return self

    def __itruediv__(self, other):
        if not other:
            return self
        if isinstance(self.dist, (list, tuple)):
            for dist in self.dist:
                dist /= other
        else:
            self.dist /= other
        return self

class Vector2d(Statistic):
    def display(self):
        import display
        if not display.all and not (self.flags & flags.printable):
            return

        d = display.VectorDisplay()
        d.__dict__.update(self.__dict__)

        if self.__dict__.has_key('ysubnames'):
            ysubnames = list(self.ysubnames)
            slack = self.x - len(ysubnames)
            if slack > 0:
                ysubnames.extend(['']*slack)
        else:
            ysubnames = range(self.x)

        for x,sname in enumerate(ysubnames):
            o = x * self.y
            d.value = self.value[o:o+self.y]
            d.name = '%s[%s]' % (self.name, sname)
            d.display()

        if self.flags & flags.total:
            d.value = []
            for y in range(self.y):
                xtot = 0.0
                for x in range(self.x):
                    xtot += self.value[y + x * self.x]
                d.value.append(xtot)

            d.name = self.name + '.total'
            d.display()

    def comparable(self, other):
        return self.name == other.name and self.x == other.x and \
               self.y == other.y

    def __eq__(self, other):
        return True

    def __isub__(self, other):
        return self

    def __iadd__(self, other):
        return self

    def __itruediv__(self, other):
        if not other:
            return self
        return self

def NewStat(source, data):
    stat = None
    if data.type == 'SCALAR':
        stat = ScalarStat()
    elif data.type == 'VECTOR':
        stat = VectorStat()
    elif data.type == 'DIST':
        stat = Dist()
    elif data.type == 'VECTORDIST':
        stat = VectorDist()
    elif data.type == 'VECTOR2D':
        stat = Vector2d()
    elif data.type == 'FORMULA':
        stat = Formula()

    stat.__dict__['source'] = source
    stat.__dict__['ticks'] = None
    stat.__dict__.update(data.__dict__)

    return stat