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Add a test with classes and lists #35

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Add a test with classes and lists #35

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cd17d38
Add a test with a class for sparse dijkstra
EmilyBourne Dec 15, 2024
c591180
Test on pasc branch
EmilyBourne Dec 15, 2024
f1627a7
Benchmark of pyccel
EmilyBourne Dec 15, 2024
d3a63ac
Update performance comparison
github-actions[bot] Dec 15, 2024
685fa1e
Update README and version
github-actions[bot] Dec 15, 2024
ef02b16
Missing continue
EmilyBourne Dec 15, 2024
fa3ae82
Benchmark of pyccel
EmilyBourne Dec 15, 2024
74b8a26
Update performance comparison
github-actions[bot] Dec 15, 2024
934a612
Update README and version
github-actions[bot] Dec 15, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/run_benchmarks.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ jobs:
- name: Install development version of pyccel from GitHub
run: |
pip3 install --upgrade pip
pip3 install 'pyccel @ git+https://github.com/pyccel/pyccel'
pip3 install 'pyccel @ git+https://github.com/pyccel/pyccel@test_pasc_branch'
- name: Install python dependencies
run: |
pip3 install .
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52 changes: 27 additions & 25 deletions README.md
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Expand Up @@ -89,38 +89,40 @@ Solves a 2D Laplace problem using Finite Differences methods. The code is adapte

Runs a molecular dynamics simulation. The code is adapted from examples written by [J. Burkardt](https://people.sc.fsu.edu/~jburkardt/py_src/py_src.html)
## Development branch results
### Performance Comparison (as of Tue Dec 10 10:35:43 UTC 2024)
### Performance Comparison (as of Sun Dec 15 11:01:13 UTC 2024)
## Compilation time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_fortran_gnu | pyccel_c_gnu | pyccel_fortran_intel | pyccel_c_intel
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann | - | 2.34 | 2.13 | 0.34 | 1.34 | 1.30 | 1.42 | 1.39
Bellman Ford | - | 3.44 | 3.69 | 1.08 | 3.64 | 3.97 | 3.80 | 4.04
Dijkstra | - | 2.40 | 2.70 | 1.60 | 3.70 | 4.01 | 3.96 | 4.12
Euler | - | 2.81 | 3.17 | 2.06 | 3.75 | 3.99 | 3.79 | 3.98
Midpoint Explicit | - | 3.20 | 3.50 | 3.10 | 3.95 | 4.24 | 4.05 | 4.19
Midpoint Fixed | - | 3.72 | 4.08 | 3.38 | 4.10 | 4.33 | 4.17 | 4.36
RK4 | - | 3.82 | 4.33 | 3.85 | 4.46 | 4.69 | 4.53 | 4.80
FD - L Convection | - | 2.41 | 2.77 | 0.94 | 1.49 | 4.06 | 1.65 | 4.09
FD - NL Convection | - | 3.42 | 3.70 | 0.92 | 1.42 | 3.99 | 1.66 | 3.94
FD - Poisson | - | 3.46 | 3.77 | 1.37 | 1.59 | 4.08 | 2.85 | 3.99
FD - Laplace | - | 6.68 | 8.16 | 3.17 | 1.84 | 4.46 | 2.14 | 4.40
M-D | - | 6.60 | 6.94 | 4.14 | - | - | - | -
Ackermann | - | 2.30 | 2.12 | 0.33 | 1.32 | 1.32 | 1.45 | 1.39
Bellman Ford | - | 3.41 | 3.68 | 1.08 | 3.70 | 3.93 | 3.78 | 3.93
Dijkstra | - | 2.38 | 2.66 | 1.56 | 3.71 | 3.98 | 3.89 | 4.04
Dijkstra with heap class | - | - | - | - | 4.23 | 4.09 | 4.61 | 4.11
Euler | - | 2.66 | 3.04 | 2.01 | 3.61 | 3.94 | 3.77 | 3.98
Midpoint Explicit | - | 3.04 | 3.43 | 2.99 | 3.85 | 4.18 | 3.98 | 4.17
Midpoint Fixed | - | 3.54 | 3.95 | 3.22 | 3.95 | 4.26 | 4.09 | 4.27
RK4 | - | 3.72 | 4.22 | 3.74 | 4.33 | 4.62 | 4.44 | 4.64
FD - L Convection | - | 2.31 | 2.65 | 0.87 | 1.40 | 3.93 | 1.59 | 3.93
FD - NL Convection | - | 3.29 | 3.54 | 0.88 | 1.41 | 3.92 | 1.62 | 3.91
FD - Poisson | - | 3.38 | 3.64 | 1.33 | 1.53 | 4.03 | 2.84 | 4.00
FD - Laplace | - | 6.49 | 7.97 | 3.01 | 1.82 | 4.34 | 2.10 | 4.28
M-D | - | 6.41 | 6.74 | 4.03 | - | - | - | -

## Execution time
Algorithm | python | pythran_gnu | pythran_intel | numba | pyccel_fortran_gnu | pyccel_c_gnu | pyccel_fortran_intel | pyccel_c_intel
------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | ------------------------- | -------------------------
Ackermann (ms) | 294.00 | 2.85 | 3.03 | 9.76 | 1.49 | 1.55 | 10.40 | 4.35
Bellman Ford (ms) | 1760.00 | 5.17 | 3.43 | 3.77 | 2.87 | 5.98 | - | 19.20
Dijkstra (ms) | 4900.00 | 26.10 | 17.00 | 19.70 | 19.30 | 31.00 | - | 23.40
Euler (ms) | 3920.00 | 25.30 | 25.80 | 38.30 | 14.80 | 145.00 | 13.70 | 128.00
Midpoint Explicit (ms) | 7980.00 | 52.50 | 51.40 | 77.40 | 23.90 | 281.00 | 16.80 | 250.00
Midpoint Fixed (ms) | 41200.00 | 251.00 | 92.60 | 374.00 | 75.60 | 1400.00 | 61.00 | 1200.00
RK4 (ms) | 19900.00 | 158.00 | 35.60 | 138.00 | 34.20 | 481.00 | 38.60 | 405.00
FD - L Convection (ms) | 2260.00 | 1.63 | 1.62 | 2.68 | 1.51 | 1.63 | - | 4.08
FD - NL Convection (ms) | 2710.00 | 1.88 | 1.63 | 2.83 | 1.97 | 2.02 | - | 4.08
FD - Poisson (ms) | 6150.00 | 2.95 | 5.46 | 7.20 | 2.77 | 3.72 | - | 4.93
FD - Laplace (ms) | 575.00 | 64.20 | 143.00 | 246.00 | 58.50 | 255.00 | - | 274.00
M-D (ms) | 14800.00 | 15.20 | 53.10 | 59.30 | - | - | - | -
Ackermann (ms) | 292.00 | 2.86 | 3.04 | 9.60 | 1.55 | 1.55 | 10.10 | 4.33
Bellman Ford (ms) | 1750.00 | 5.22 | 3.41 | 3.90 | 2.94 | 6.09 | - | 18.90
Dijkstra (ms) | 4870.00 | 25.30 | 16.80 | 18.90 | 18.30 | 29.30 | - | 21.00
Dijkstra with heap class (ms) | 8920.00 | - | - | - | 264.00 | 102.00 | - | 107.00
Euler (ms) | 3790.00 | 25.60 | 25.40 | 36.90 | 14.40 | 141.00 | 13.80 | 127.00
Midpoint Explicit (ms) | 7630.00 | 52.70 | 51.20 | 78.00 | 22.10 | 279.00 | 16.20 | 251.00
Midpoint Fixed (ms) | 39500.00 | 252.00 | 92.50 | 376.00 | 74.40 | 1400.00 | 58.90 | 1200.00
RK4 (ms) | 21000.00 | 161.00 | 36.80 | 139.00 | 31.50 | 485.00 | 38.40 | 402.00
FD - L Convection (ms) | 2260.00 | 1.63 | 1.60 | 2.67 | 1.54 | 1.85 | - | 4.06
FD - NL Convection (ms) | 2720.00 | 1.82 | 1.62 | 2.77 | 1.83 | 2.03 | - | 4.09
FD - Poisson (ms) | 6020.00 | 2.95 | 5.45 | 7.10 | 2.75 | 3.84 | - | 4.95
FD - Laplace (ms) | 578.00 | 64.70 | 143.00 | 244.00 | 58.10 | 280.00 | - | 273.00
M-D (ms) | 14600.00 | 15.30 | 52.90 | 58.90 | - | - | - | -

![Development compilation results](./version_specific_results/devel_performance_310_compilation.svg)
![Development execution results](./version_specific_results/devel_performance_310_execution.svg)
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9 changes: 9 additions & 0 deletions benchmarks/run_benchmarks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -82,6 +82,11 @@
['dijkstra_distance_test'],
'',
'd = dijkstra_distance_test()'),
TestInfo('Dijkstra with heap class',
'dijkstra_heap.py',
['dijkstra'],
'from setup_tools import setup_sparse_dijkstra; graph, start, num_nodes, max_neighbours = setup_sparse_dijkstra()',
'd = dijkstra(graph, start, num_nodes, max_neighbours)'),
TestInfo('Euler',
'euler_mod.py',
['euler_humps_test'],
Expand Down Expand Up @@ -181,6 +186,8 @@ def run_process(cmd: "List[str]", time_compilation: "bool"=False, env = None):
])
return returncode, out, err, cpu_time

setup_basename = 'setup_tools.py'
setup_file = os.path.join(code_folder, setup_basename)

for t in tests:
print("===========================================", file=log_file, flush=True)
Expand All @@ -200,6 +207,7 @@ def run_process(cmd: "List[str]", time_compilation: "bool"=False, env = None):
os.makedirs(new_folder, exist_ok=True)
shutil.copyfile(test_file, os.path.join(new_folder, basename))
shutil.copyfile(numba_test_file, os.path.join(new_folder, numba_basename))
shutil.copyfile(setup_file, os.path.join(new_folder, setup_basename))
os.chdir(new_folder)

import_funcs = ', '.join(t.imports)
Expand Down Expand Up @@ -273,6 +281,7 @@ def run_process(cmd: "List[str]", time_compilation: "bool"=False, env = None):
run_times.append(None)
run_units.append(None)
print(err, file=log_file, flush=True)
continue
else:
print("Compilation Process time : ",out, file=log_file, flush=True)
comp_times.append('{:.2f}'.format(float(out)))
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94 changes: 94 additions & 0 deletions benchmarks/tests/dijkstra_heap.py
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@@ -0,0 +1,94 @@
import numpy as np

class MinHeap:
def __init__(self):
self.distances: 'list[int]' = []
self.nodes: 'list[int]' = []

def _sift_up(self, index: int):
parent = (index - 1) // 2
if index > 0 and self.distances[index] < self.distances[parent]:
tmp = self.distances[index]
self.distances[index] = self.distances[parent]
self.distances[parent] = tmp
tmp = self.nodes[index]
self.nodes[index] = self.nodes[parent]
self.nodes[parent] = tmp
self._sift_up(parent)

def _sift_down(self, index: int):
left = 2 * index + 1
right = 2 * index + 2
smallest = index

if left < len(self.distances) and self.distances[left] < self.distances[smallest]:
smallest = left
if right < len(self.distances) and self.distances[right] < self.distances[smallest]:
smallest = right

if smallest != index:
tmp = self.distances[index]
self.distances[index] = self.distances[smallest]
self.distances[smallest] = tmp
tmp = self.nodes[index]
self.nodes[index] = self.nodes[smallest]
self.nodes[smallest] = tmp
self._sift_down(smallest)

def push(self, item_distance: 'int', item_node: 'int'):
self.distances.append(item_distance)
self.nodes.append(item_node)
self._sift_up(len(self.distances) - 1)

def pop(self):
if len(self.distances) == 1:
return self.distances.pop(), self.nodes.pop()
root_distance = self.distances[0]
root_node = self.nodes[0]
self.distances[0] = self.distances.pop()
self.nodes[0] = self.nodes.pop()
self._sift_down(0)
return root_distance, root_node


def length(self) -> int:
return len(self.distances)

def dijkstra(graph: 'int[:,:]', start: int, num_nodes: int, max_neighbors: int) -> 'int[:]':
"""
Implements Dijkstra's algorithm to find the shortest paths from a start node.

Parameters:
graph (numpy array): A 2D numpy array where each row represents a node and contains up to 5 neighbors as pairs (neighbor, weight). -1 indicates no neighbor.
start (int): The starting node index.
num_nodes (int): Total number of nodes in the graph.

Returns:
numpy array: Shortest distances from the start node to each node.
"""
priority_queue = MinHeap()
shortest_distances = np.full(num_nodes, int(1e9))
shortest_distances[start] = 0

priority_queue.push(0, start)

while priority_queue.length() > 0:
current_distance, current_node = priority_queue.pop()

if current_distance > shortest_distances[current_node]:
continue

for i in range(0, 2 * max_neighbors, 2):
neighbor = graph[current_node, i]
weight = graph[current_node, i + 1]
if neighbor == -1 or current_node == neighbor:
continue

dist = current_distance + weight

if dist < shortest_distances[neighbor]:
shortest_distances[neighbor] = dist
priority_queue.push(dist, neighbor)

return shortest_distances

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