make the whole project more python idiomatic

* rename src directory to allocbench
* make global variable names UPPERCASE
* format a lot of code using yapf
* use lowercase ld_preload and ld_library_path as Allocator members
* name expected Errors 'err' and don't raise a new Exception
* disable some pylint messages

1 files changed, 0 insertions, 117 deletions

diff --git a/src/benchmarks/loop.py b/src/benchmarks/loop.py
deleted file mode 100644
index 030552b..0000000
--- a/src/benchmarks/loop.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# Copyright 2018-2020 Florian Fischer <florian.fl.fischer@fau.de>
-#
-# This file is part of allocbench.
-#
-# allocbench is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# allocbench is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with allocbench.  If not, see <http://www.gnu.org/licenses/>.
-"""Definition of the loop micro benchmark
-
-This benchmark allocates and immediately deallocates a pseudo random sized allocation
-N times in T threads. The acquired memory is neither read nor written. Not using the
-allocations at all maybe seems odd but this micro benchmark should only measure
-the allocators fast paths, scalability and management overhead.
-Using the allocations will add cache effects to our results which are
-measured for example in the false sharing or larson benchmarks.
-
-Observations:
-* Glibc's factor two faster for allocations <= 1024B
-* TCMalloc suffers when allocating only small chunks
-
-Interpretation:
-* A significant higher cache miss rate than other allocators could mean that
-  internals suffer from false sharing (TCMalloc).
-* Speed changes with constant threads but changing sizes may show performance
-  differences in differing strategies for seperate sizes (glibc thread caches < 1032B)
-"""
-
-from src.benchmark import Benchmark
-import src.plots as plt
-
-
-class BenchmarkLoop(Benchmark):
-    """Loop micro benchmark
-
-    This benchmark allocates and frees n blocks in t concurrent threads.
-    """
-    def __init__(self):
-        name = "loop"
-
-        self.cmd = "loop{binary_suffix} {threads} 1000000 {maxsize}"
-
-        self.args = {
-            "maxsize": [2**x for x in range(6, 16)],
-            "threads": Benchmark.scale_threads_for_cpus(2)
-        }
-
-        self.requirements = ["loop"]
-        super().__init__(name)
-
-    def process_output(self, result, stdout, stderr, alloc, perm):
-        result["mops"] = perm.threads / float(result["task-clock"])
-
-    def summary(self):
-        # Speed
-        plt.plot(
-            self,
-            "{mops}",
-            fig_options={
-                'ylabel': 'MOPS/cpu-second',
-                'title': 'Loop: {fixed_part_str}',
-                'autoticks': False,
-            },
-            file_postfix="time")
-
-        # L1 cache misses
-        plt.plot(
-            self,
-            "({L1-dcache-load-misses}/{L1-dcache-loads})*100",
-            fig_options={
-                'ylabel': "L1 misses in %",
-                'title': "Loop l1 cache misses: {fixed_part_str}",
-                'autoticks': False,
-            },
-            file_postfix="l1misses")
-
-        # Speed Matrix
-        plt.write_best_doublearg_tex_table(
-            self,
-            "{mops}",
-            file_postfix="time.matrix")
-
-        plt.write_tex_table(
-            self, 
-            [{
-                "label": "MOPS/s",
-                "expression": "{mops}",
-                "sort": ">"
-            }],
-            file_postfix="mops.table")
-
-        plt.export_stats_to_csv(self, "task-clock")
-        plt.export_stats_to_dataref(self, "task-clock")
-
-        # pgfplot test
-        plt.pgfplot(self,
-                    self.iterate_args(fixed={"maxsize": 1024}, args=self.results["args"]),
-                    "int(perm.threads)",
-                    "{mops}",
-                    xlabel="Threads",
-                    ylabel="MOPS/cpu-second",
-                    title="Loop: 1024B",
-                    postfix='mops_1024B')
-
-        # create pgfplot legend
-        plt.pgfplot_legend(self)
-
-
-loop = BenchmarkLoop()