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guanmingchiu pushed a commit to branch dev-qdp
in repository https://gitbox.apache.org/repos/asf/mahout.git
The following commit(s) were added to refs/heads/dev-qdp by this push:
new c614ee101 [QDP] improve e2e benchmark (#713)
c614ee101 is described below
commit c614ee101ae007fb38028d9a3d491fbce5a12f04
Author: KUAN-HAO HUANG <[email protected]>
AuthorDate: Fri Dec 12 16:11:56 2025 +0800
[QDP] improve e2e benchmark (#713)
* improve e2e benchmark
* gc.coolect first
---
.../{benchmark_e2e_final.py => benchmark_e2e.py} | 54 +++++++++++++++++++++-
1 file changed, 52 insertions(+), 2 deletions(-)
diff --git a/qdp/benchmark/benchmark_e2e_final.py
b/qdp/benchmark/benchmark_e2e.py
similarity index 92%
rename from qdp/benchmark/benchmark_e2e_final.py
rename to qdp/benchmark/benchmark_e2e.py
index c00476b2d..b72c81d1f 100644
--- a/qdp/benchmark/benchmark_e2e_final.py
+++ b/qdp/benchmark/benchmark_e2e.py
@@ -35,6 +35,7 @@ import torch.nn as nn
import numpy as np
import os
import itertools
+import gc
import pyarrow as pa
import pyarrow.parquet as pq
import pyarrow.ipc as ipc
@@ -63,6 +64,14 @@ HIDDEN_DIM = 16
BATCH_SIZE = 64 # Small batch to stress loop overhead
+def clean_cache():
+ """Clear GPU cache and Python garbage collection."""
+ gc.collect()
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ torch.cuda.synchronize()
+
+
class DummyQNN(nn.Module):
def __init__(self, n_qubits):
super().__init__()
@@ -102,6 +111,9 @@ def generate_data(n_qubits, n_samples):
print(f" Generated {n_samples} samples")
print(f" Parquet: {parquet_size:.2f} MB, Arrow IPC: {arrow_size:.2f} MB")
+ # Clean cache after data generation
+ clean_cache()
+
# -----------------------------------------------------------
# 1. Qiskit Full Pipeline
@@ -111,6 +123,9 @@ def run_qiskit(n_qubits, n_samples):
print("\n[Qiskit] Not installed, skipping.")
return 0.0, None
+ # Clean cache before starting benchmark
+ clean_cache()
+
print("\n[Qiskit] Full Pipeline (Disk -> GPU)...")
model = DummyQNN(n_qubits).cuda()
backend = AerSimulator(method="statevector")
@@ -162,6 +177,10 @@ def run_qiskit(n_qubits, n_samples):
print(f"\n Total Time: {total_time:.4f} s")
all_qiskit_tensor = torch.cat(all_qiskit_states, dim=0)
+
+ # Clean cache after benchmark completion
+ clean_cache()
+
return total_time, all_qiskit_tensor
@@ -173,6 +192,9 @@ def run_pennylane(n_qubits, n_samples):
print("\n[PennyLane] Not installed, skipping.")
return 0.0, None
+ # Clean cache before starting benchmark
+ clean_cache()
+
print("\n[PennyLane] Full Pipeline (Disk -> GPU)...")
dev = qml.device("default.qubit", wires=n_qubits)
@@ -224,6 +246,9 @@ def run_pennylane(n_qubits, n_samples):
all_pl_states, dim=0
) # Should handle cases where last batch is smaller
+ # Clean cache after benchmark completion
+ clean_cache()
+
return total_time, all_pl_states_tensor
@@ -231,6 +256,9 @@ def run_pennylane(n_qubits, n_samples):
# 3. Mahout Parquet Pipeline
# -----------------------------------------------------------
def run_mahout_parquet(engine, n_qubits, n_samples):
+ # Clean cache before starting benchmark
+ clean_cache()
+
print("\n[Mahout-Parquet] Full Pipeline (Parquet -> GPU)...")
model = DummyQNN(n_qubits).cuda()
@@ -251,10 +279,10 @@ def run_mahout_parquet(engine, n_qubits, n_samples):
# Reshape to [n_samples, state_len] (still complex)
state_len = 1 << n_qubits
- gpu_reshaped = gpu_batched.view(n_samples, state_len)
# Convert to float for model (batch already on GPU)
reshape_start = time.perf_counter()
+ gpu_reshaped = gpu_batched.view(n_samples, state_len)
gpu_all_data = gpu_reshaped.abs().to(torch.float32)
reshape_time = time.perf_counter() - reshape_start
print(f" Reshape & convert: {reshape_time:.4f} s")
@@ -267,6 +295,10 @@ def run_mahout_parquet(engine, n_qubits, n_samples):
torch.cuda.synchronize()
total_time = time.perf_counter() - start_time
print(f" Total Time: {total_time:.4f} s")
+
+ # Clean cache after benchmark completion
+ clean_cache()
+
return total_time, gpu_reshaped
@@ -274,6 +306,9 @@ def run_mahout_parquet(engine, n_qubits, n_samples):
# 4. Mahout Arrow IPC Pipeline
# -----------------------------------------------------------
def run_mahout_arrow(engine, n_qubits, n_samples):
+ # Clean cache before starting benchmark
+ clean_cache()
+
print("\n[Mahout-Arrow] Full Pipeline (Arrow IPC -> GPU)...")
model = DummyQNN(n_qubits).cuda()
@@ -291,9 +326,9 @@ def run_mahout_arrow(engine, n_qubits, n_samples):
print(f" DLPack conversion: {dlpack_time:.4f} s")
state_len = 1 << n_qubits
- gpu_reshaped = gpu_batched.view(n_samples, state_len)
reshape_start = time.perf_counter()
+ gpu_reshaped = gpu_batched.view(n_samples, state_len)
gpu_all_data = gpu_reshaped.abs().to(torch.float32)
reshape_time = time.perf_counter() - reshape_start
print(f" Reshape & convert: {reshape_time:.4f} s")
@@ -305,6 +340,10 @@ def run_mahout_arrow(engine, n_qubits, n_samples):
torch.cuda.synchronize()
total_time = time.perf_counter() - start_time
print(f" Total Time: {total_time:.4f} s")
+
+ # Clean cache after benchmark completion
+ clean_cache()
+
return total_time, gpu_reshaped
@@ -378,6 +417,9 @@ if __name__ == "__main__":
print(f"Mahout Init Error: {e}")
exit(1)
+ # Clean cache before starting benchmarks
+ clean_cache()
+
print("\n" + "=" * 70)
print(f"E2E BENCHMARK: {args.qubits} Qubits, {args.samples} Samples")
print("=" * 70)
@@ -391,19 +433,27 @@ if __name__ == "__main__":
# Run benchmarks
if "pennylane" in args.frameworks:
t_pl, pl_all_states = run_pennylane(args.qubits, args.samples)
+ # Clean cache between framework benchmarks
+ clean_cache()
if "qiskit" in args.frameworks:
t_qiskit, qiskit_all_states = run_qiskit(args.qubits, args.samples)
+ # Clean cache between framework benchmarks
+ clean_cache()
if "mahout-parquet" in args.frameworks:
t_mahout_parquet, mahout_parquet_all_states = run_mahout_parquet(
engine, args.qubits, args.samples
)
+ # Clean cache between framework benchmarks
+ clean_cache()
if "mahout-arrow" in args.frameworks:
t_mahout_arrow, mahout_arrow_all_states = run_mahout_arrow(
engine, args.qubits, args.samples
)
+ # Clean cache between framework benchmarks
+ clean_cache()
print("\n" + "=" * 70)
print("E2E LATENCY (Lower is Better)")
