USER
Our current benchmark only shows results for a single core, we need to see both for the single core and the multi thread score, code : import time
import numpy as np
import argparse
import psutil
from numba import njit, prange
def get_cpu_frequencies():
"""
Retrieve the current, minimum, and maximum CPU frequencies for each core.
"""
freq = psutil.cpu_freq(percpu=True)
frequencies = {
'current': [f.current for f in freq if f],
'min': [f.min for f in freq if f],
'max': [f.max for f in freq if f],
}
return frequencies
@njit(parallel=True, fastmath=True, cache=True)
def benchmark_fp_operation(x_fp, y_fp, num_iterations, operation):
"""
Benchmark a specific floating-point operation using Numba's JIT compilation and parallelization.
Parameters:
- x_fp: NumPy array of float64.
- y_fp: NumPy array of float64.
- num_iterations: Number of iterations to perform.
- operation: String indicating the operation ('add', 'sub', 'mul', 'div').
Returns:
- total: Accumulated result to prevent loop optimization.
"""
total = 0.0
for i in prange(num_iterations):
idx = i % x_fp.size
if operation == 'add':
a = x_fp[idx] + y_fp[idx]
total += a
elif operation == 'sub':
b = x_fp[idx] - y_fp[idx]
total += b
elif operation == 'mul':
c = x_fp[idx] * y_fp[idx]
total += c
elif operation == 'div':
d = x_fp[idx] / y_fp[idx] # Assuming y_fp has no zeros
total += d
return total
@njit(parallel=True, fastmath=True, cache=True)
def benchmark_int_operation(x_int, y_int, num_iterations, operation):
"""
Benchmark a specific integer operation using Numba's JIT compilation and parallelization.
Parameters:
- x_int: NumPy array of int64.
- y_int: NumPy array of int64.
- num_iterations: Number of iterations to perform.
- operation: String indicating the operation ('add', 'sub', 'mul', 'div').
Returns:
- total: Accumulated result to prevent loop optimization.
"""
total = 0
for i in prange(num_iterations):
idx = i % x_int.size
if operation == 'add':
a = x_int[idx] + y_int[idx]
total += a
elif operation == 'sub':
b = x_int[idx] - y_int[idx]
total += b
elif operation == 'mul':
c = x_int[idx] * y_int[idx]
total += c
elif operation == 'div':
d = x_int[idx] // y_int[idx] # Assuming y_int has no zeros
total += d
return total
def single_operation_benchmark(op_type, operation, num_iterations, x_array, y_array):
"""
Perform a benchmark for a specific operation type.
Parameters:
- op_type: 'FP' for floating-point or 'INT' for integer operations.
- operation: 'add', 'sub', 'mul', 'div'.
- num_iterations: Number of iterations to perform.
- x_array: NumPy array for the first operand.
- y_array: NumPy array for the second operand.
Returns:
- gops: Giga Operations Per Second.
"""
start_time = time.perf_counter()
if op_type == "FP":
total = benchmark_fp_operation(x_array, y_array, num_iterations, operation)
elif op_type == "INT":
total = benchmark_int_operation(x_array, y_array, num_iterations, operation)
else:
raise ValueError("Invalid operation type")
end_time = time.perf_counter()
elapsed_time = end_time - start_time
# Each iteration performs one operation
ops = num_iterations / elapsed_time # Operations per second
gops = ops / 1e9 # Convert to Giga Operations Per Second
return gops, elapsed_time, total # Return total to prevent optimization
def main():
parser = argparse.ArgumentParser(description="Enhanced CPU Benchmarking Tool with IPC Estimation")
parser.add_argument("--iterations", type=int, default=100_000_000, help="Number of iterations per benchmark")
parser.add_argument("--threads", type=int, default=psutil.cpu_count(logical=False), help="Number of CPU cores to use for benchmarking")
args = parser.parse_args()
num_iterations = args.iterations
num_threads = args.threads
instructions_per_op = 1 # 1 instruction per operation
# Pre-generate random data for benchmarking
print("Pre-generating random data for benchmarks...")
np.random.seed(42) # For reproducibility
size = 1_000_000 # Size of arrays (adjusted for better cache performance)
# Ensure no zeros to prevent division by zero
x_fp = np.random.uniform(low=1.0, high=100.0, size=size).astype(np.float64)
y_fp = np.random.uniform(low=1.0, high=100.0, size=size).astype(np.float64)
x_int = np.random.randint(low=1, high=1_000_000, size=size).astype(np.int64)
y_int = np.random.randint(low=1, high=1_000_000, size=size).astype(np.int64)
print("Data generation complete.\n")
# Warm-up compilation
print("Warming up Numba-compiled functions...")
benchmark_fp_operation(x_fp[:1000], y_fp[:1000], 1000, 'add')
benchmark_fp_operation(x_fp[:1000], y_fp[:1000], 1000, 'sub')
benchmark_fp_operation(x_fp[:1000], y_fp[:1000], 1000, 'mul')
benchmark_fp_operation(x_fp[:1000], y_fp[:1000], 1000, 'div')
benchmark_int_operation(x_int[:1000], y_int[:1000], 1000, 'add')
benchmark_int_operation(x_int[:1000], y_int[:1000], 1000, 'sub')
benchmark_int_operation(x_int[:1000], y_int[:1000], 1000, 'mul')
benchmark_int_operation(x_int[:1000], y_int[:1000], 1000, 'div')
print("Warm-up complete.\n")
# Retrieve CPU frequencies before benchmarks
print("Retrieving CPU frequencies before benchmarks...")
frequencies_before = get_cpu_frequencies()
if frequencies_before['current']:
avg_freq_before = sum(frequencies_before['current']) / len(frequencies_before['current']) / 1000 # Convert MHz to GHz
print(f"Average CPU Frequency (Before): {avg_freq_before:.2f} GHz\n")
else:
print("CPU frequency information is unavailable.\n")
avg_freq_before = None
# Define operations to benchmark
operations = ['add', 'sub', 'mul', 'div']
op_types = ['FP', 'INT']
# Perform benchmarks
results = {op_type: {} for op_type in op_types}
for op_type in op_types:
for operation in operations:
print(f"Running {op_type} - {operation} benchmark...")
if op_type == "FP":
gops, elapsed, total = single_operation_benchmark(op_type, operation, num_iterations, x_fp, y_fp)
else:
gops, elapsed, total = single_operation_benchmark(op_type, operation, num_iterations, x_int, y_int)
results[op_type][operation] = {
'GOPS': gops,
'Elapsed Time (s)': elapsed,
'Total': total
}
print(f" {operation.capitalize()} GOPS: {gops:.3f} GOPS in {elapsed:.2f} seconds\n")
# Retrieve CPU frequencies after benchmarks
print("Retrieving CPU frequencies after benchmarks...")
frequencies_after = get_cpu_frequencies()
if frequencies_after['current']:
avg_freq_after = sum(frequencies_after['current']) / len(frequencies_after['current']) / 1000 # GHz
print(f"Average CPU Frequency (After Benchmarks): {avg_freq_after:.2f} GHz\n")
else:
print("CPU frequency information is unavailable after benchmarks.\n")
avg_freq_after = None
# Calculate IPC
ipc_results = {op_type: {} for op_type in op_types}
for op_type in op_types:
for operation in operations:
gops = results[op_type][operation]['GOPS']
if op_type == "FP":
freq = avg_freq_before # Assuming frequency remains stable
else:
freq = avg_freq_before
if freq:
ipc = (gops * 1e9 * instructions_per_op) / (freq * 1e9)
ipc_results[op_type][operation] = ipc
else:
ipc_results[op_type][operation] = float('nan')
# Display Results
print("Benchmark Results:\n")
for op_type in op_types:
print(f"{op_type}-Type Operations:")
for operation in operations:
gops = results[op_type][operation]['GOPS']
ipc = ipc_results[op_type][operation]
print(f" {operation.capitalize()}: {gops:.3f} GOPS, Estimated IPC: {ipc:.2f}")
print("\n")
print(f"CPU Frequencies:")
if avg_freq_before:
print(f" Before Benchmarks: {avg_freq_before:.2f} GHz")
else:
print(" Before Benchmarks: N/A")
if frequencies_after['current']:
print(f" After Benchmarks: {avg_freq_after:.2f} GHz")
else:
print(" After Benchmarks: N/A")
if __name__ == "__main__":
main()ASSISTANT
To enhance your benchmarking tool to display both single-core and multi-threaded scores, you'll need to modify the existing script to perform two separate benchmarking runs:
1. **Single-Core Benchmark**: Configure Numba to use only one thread.
2. **Multi-Threaded Benchmark**: Configure Numba to utilize all available CPU cores.
Here's how you can achieve this:
1. **Set Numba's Number of Threads**: Numba allows you to control the number of threads it uses via the `NUMBA_NUM_THREADS` configuration. We'll adjust this setting before each benchmarking run.
2. **Compile Functions Separately**: To ensure that functions are recompiled with the correct thread settings, we'll use different Numba signatures for single-threaded and multi-threaded runs.
3. **Organize Results**: Store and display results for both single-core and multi-threaded benchmarks separately for clarity.
Below is the updated script with the necessary modifications:
```python
import time
import numpy as np
import argparse
import psutil
from numba import njit, prange, config
def get_cpu_frequencies():
"""
Retrieve the current, minimum, and maximum CPU frequencies for each core.
"""
freq = psutil.cpu_freq(percpu=True)
frequencies = {
'current': [f.current for f in freq if f],
'min': [f.min for f in freq if f],
'max': [f.max for f in freq if f],
}
return frequencies
@njit(parallel=True, fastmath=True, cache=True)
def benchmark_fp_operation(x_fp, y_fp, num_iterations, operation):
"""
Benchmark a specific floating-point operation using Numba's JIT compilation and parallelization.
Parameters:
- x_fp: NumPy array of float64.
- y_fp: NumPy array of float64.
- num_iterations: Number of iterations to perform.
- operation: String indicating the operation ('add', 'sub', 'mul', 'div').
Returns:
- total: Accumulated result to prevent loop optimization.
"""
total = 0.0
for i in prange(num_iterations):
idx = i % x_fp.size
if operation == 'add':
a = x_fp[idx] + y_fp[idx]
total += a
elif operation == 'sub':
b = x_fp[idx] - y_fp[idx]
total += b
elif operation == 'mul':
c = x_fp[idx] * y_fp[idx]
total += c
elif operation == 'div':
d = x_fp[idx] / y_fp[idx] # Assuming y_fp has no zeros
total += d
return total
@njit(parallel=True, fastmath=True, cache=True)
def benchmark_int_operation(x_int, y_int, num_iterations, operation):
"""
Benchmark a specific integer operation using Numba's JIT compilation and parallelization.
Parameters:
- x_int: NumPy array of int64.
- y_int: NumPy array of int64.
- num_iterations: Number of iterations to perform.
- operation: String indicating the operation ('add', 'sub', 'mul', 'div').
Returns:
- total: Accumulated result to prevent loop optimization.
"""
total = 0
for i in prange(num_iterations):
idx = i % x_int.size
if operation == 'add':
a = x_int[idx] + y_int[idx]
total += a
elif operation == 'sub':
b = x_int[idx] - y_int[idx]
total += b
elif operation == 'mul':
c = x_int[idx] * y_int[idx]
total += c
elif operation == 'div':
d = x_int[idx] // y_int[idx] # Assuming y_int has no zeros
total += d
return total
def single_operation_benchmark(op_type, operation, num_iterations, x_array, y_array):
"""
Perform a benchmark for a specific operation type.
Parameters:
- op_type: 'FP' for floating-point or 'INT' for integer operations.
- operation: 'add', 'sub', 'mul', 'div'.
- num_iterations: Number of iterations to perform.
- x_array: NumPy array for the first operand.
- y_array: NumPy array for the second operand.
Returns:
- gops: Giga Operations Per Second.
"""
start_time = time.perf_counter()
if op_type == "FP":
total = benchmark_fp_operation(x_array, y_array, num_iterations, operation)
elif op_type == "INT":
total = benchmark_int_operation(x_array, y_array, num_iterations, operation)
else:
raise ValueError("Invalid operation type")
end_time = time.perf_counter()
elapsed_time = end_time - start_time
# Each iteration performs one operation
ops = num_iterations / elapsed_time # Operations per second
gops = ops / 1e9 # Convert to Giga Operations Per Second
return gops, elapsed_time, total # Return total to prevent optimization
def main():
parser = argparse.ArgumentParser(description="Enhanced CPU Benchmarking Tool with IPC Estimation")
parser.add_argument("--iterations", type=int, default=100_000_000, help="Number of iterations per benchmark")
args = parser.parse_args()
num_iterations = args.iterations
instructions_per_op = 1 # 1 instruction per operation
# Pre-generate random data for benchmarking
print("Pre-generating random data for benchmarks...")
np.random.seed(42) # For reproducibility
size = 1_000_000 # Size of arrays (adjusted for better cache performance)
# Ensure no zeros to prevent division by zero
x_fp = np.random.uniform(low=1.0, high=100.0, size=size).astype(np.float64)
y_fp = np.random.uniform(low=1.0, high=100.0, size=size).astype(np.float64)
x_int = np.random.randint(low=1, high=1_000_000, size=size).astype(np.int64)
y_int = np.random.randint(low=1, high=1_000_000, size=size).astype(np.int64)
print("Data generation complete.\n")
# Define operations to benchmark
operations = ['add', 'sub', 'mul', 'div']
op_types = ['FP', 'INT']
# Define threading configurations
threading_configs = {
'Single-Core': 1,
'Multi-Threaded': psutil.cpu_count(logical=False)
}
# Warm-up is done per threading configuration to ensure correct compilation
# Define a separate benchmarking process for each threading config
all_results = {}
for config_name, num_threads in threading_configs.items():
print(f"--- {config_name} Benchmark ---")
print(f"Setting Numba to use {num_threads} thread(s)...")
config.NUMBA_NUM_THREADS = num_threads
# Update number of threads in Numba's thread pool
from numba import _config
_config.NUMBA_NUM_THREADS = num_threads
# Clear Numba's cache to ensure recompilation with new thread settings
benchmark_fp_operation.enable_caching = False
benchmark_int_operation.enable_caching = False
# Warm-up compilation
print("Warming up Numba-compiled functions...")
if config_name == 'Single-Core':
benchmark_fp_operation_single = benchmark_fp_operation
benchmark_int_operation_single = benchmark_int_operation
elif config_name == 'Multi-Threaded':
benchmark_fp_operation_multi = benchmark_fp_operation
benchmark_int_operation_multi = benchmark_int_operation
benchmark_fp_operation(x_fp[:1000], y_fp[:1000], 1000, 'add')
benchmark_fp_operation(x_fp[:1000], y_fp[:1000], 1000, 'sub')
benchmark_fp_operation(x_fp[:1000], y_fp[:1000], 1000, 'mul')
benchmark_fp_operation(x_fp[:1000], y_fp[:1000], 1000, 'div')
benchmark_int_operation(x_int[:1000], y_int[:1000], 1000, 'add')
benchmark_int_operation(x_int[:1000], y_int[:1000], 1000, 'sub')
benchmark_int_operation(x_int[:1000], y_int[:1000], 1000, 'mul')
benchmark_int_operation(x_int[:1000], y_int[:1000], 1000, 'div')
print("Warm-up complete.\n")
# Retrieve CPU frequencies before benchmarks
print("Retrieving CPU frequencies before benchmarks...")
frequencies_before = get_cpu_frequencies()
if frequencies_before['current']:
avg_freq_before = sum(frequencies_before['current']) / len(frequencies_before['current']) / 1000 # Convert MHz to GHz
print(f"Average CPU Frequency (Before): {avg_freq_before:.2f} GHz\n")
else:
print("CPU frequency information is unavailable.\n")
avg_freq_before = None
# Perform benchmarks
results = {op_type: {} for op_type in op_types}
for op_type in op_types:
for operation in operations:
print(f"Running {op_type} - {operation} benchmark...")
if op_type == "FP":
gops, elapsed, total = single_operation_benchmark(op_type, operation, num_iterations, x_fp, y_fp)
else:
gops, elapsed, total = single_operation_benchmark(op_type, operation, num_iterations, x_int, y_int)
results[op_type][operation] = {
'GOPS': gops,
'Elapsed Time (s)': elapsed,
'Total': total
}
print(f" {operation.capitalize()} GOPS: {gops:.3f} GOPS in {elapsed:.2f} seconds\n")
# Retrieve CPU frequencies after benchmarks
print("Retrieving CPU frequencies after benchmarks...")
frequencies_after = get_cpu_frequencies()
if frequencies_after['current']:
avg_freq_after = sum(frequencies_after['current']) / len(frequencies_after['current']) / 1000 # GHz
print(f"Average CPU Frequency (After Benchmarks): {avg_freq_after:.2f} GHz\n")
else:
print("CPU frequency information is unavailable after benchmarks.\n")
avg_freq_after = None
# Calculate IPC
ipc_results = {op_type: {} for op_type in op_types}
for op_type in op_types:
for operation in operations:
gops = results[op_type][operation]['GOPS']
if avg_freq_before:
ipc = (gops * 1e9 * instructions_per_op) / (avg_freq_before * 1e9)
ipc_results[op_type][operation] = ipc
else:
ipc_results[op_type][operation] = float('nan')
# Store results
all_results[config_name] = {
'Results': results,
'IPC': ipc_results,
'Frequencies Before': avg_freq_before,
'Frequencies After': avg_freq_after
}
# Reset Numba threads for next configuration if any (not necessary here since only two configs)
print("\n" + "="*50 + "\n")
# Display Results
for config_name, data in all_results.items():
print(f"=== {config_name} Benchmark Results ===\n")
results = data['Results']
ipc_results = data['IPC']
for op_type in op_types:
print(f"{op_type}-Type Operations:")
for operation in operations:
gops = results[op_type][operation]['GOPS']
ipc = ipc_results[op_type][operation]
print(f" {operation.capitalize()}: {gops:.3f} GOPS, Estimated IPC: {ipc:.2f}")
print("\n")
print(f"CPU Frequencies for {config_name} Benchmark:")
if data['Frequencies Before']:
print(f" Before Benchmarks: {data['Frequencies Before']:.2f} GHz")
else:
print(" Before Benchmarks: N/A")
if data['Frequencies After']:
print(f" After Benchmarks: {data['Frequencies After']:.2f} GHz")
else:
print(" After Benchmarks: N/A")
print("\n" + "="*50 + "\n")
if __name__ == "__main__":
main()
```
### Key Modifications and Explanations
1. **Threading Configurations**:
- **`threading_configs` Dictionary**: Defines two configurations—`Single-Core` with 1 thread and `Multi-Threaded` with the maximum number of physical cores available.
```python
threading_configs = {
'Single-Core': 1,
'Multi-Threaded': psutil.cpu_count(logical=False)
}
```
2. **Setting Numba's Number of Threads**:
- Before each benchmarking run, set the number of threads Numba should use.
- **`config.NUMBA_NUM_THREADS = num_threads`**: Updates the number of threads.
- **`_config.NUMBA_NUM_THREADS = num_threads`**: Ensures internal Numba configurations are updated.
```python
from numba import _config
_config.NUMBA_NUM_THREADS = num_threads
```
3. **Clearing Numba's Cache**:
- To ensure that functions are recompiled with the new thread settings, disable caching temporarily.
```python
benchmark_fp_operation.enable_caching = False
benchmark_int_operation.enable_caching = False
```
4. **Warm-Up Runs**:
- Perform warm-up runs for each threading configuration to allow Numba to compile the functions with the current thread settings.
5. **Storing and Displaying Results Separately**:
- **`all_results` Dictionary**: Stores results for both single-core and multi-threaded benchmarks.
- Results are displayed separately for clarity, showing GOPS and estimated IPC for each operation type under each threading configuration.
```python
all_results[config_name] = {
'Results': results,
'IPC': ipc_results,
'Frequencies Before': avg_freq_before,
'Frequencies After': avg_freq_after
}
```
6. **Handling IPC Calculation**:
- IPC (Instructions Per Cycle) is estimated based on GOPS and CPU frequency.
```python
ipc = (gops * 1e9 * instructions_per_op) / (avg_freq_before * 1e9)
ipc_results[op_type][operation] = ipc
```
7. **Final Output**:
- The script now outputs benchmark results for both single-core and multi-threaded runs, including GOPS, estimated IPC, and CPU frequency before and after benchmarks.
### Sample Output
Upon running the script, you can expect an output structure similar to this:
```
Pre-generating random data for benchmarks...
Data generation complete.
--- Single-Core Benchmark ---
Setting Numba to use 1 thread(s)...
Warming up Numba-compiled functions...
Warm-up complete.
Retrieving CPU frequencies before benchmarks...
Average CPU Frequency (Before): 3.60 GHz
Running FP - add benchmark...
Add GOPS: 0.500 GOPS in 200.00 seconds
...
Benchmark Results:
Single-Core-Type Operations:
Add: 0.500 GOPS, Estimated IPC: 0.14
Sub: 0.480 GOPS, Estimated IPC: 0.13
Mul: 0.450 GOPS, Estimated IPC: 0.13
Div: 0.300 GOPS, Estimated IPC: 0.08
CPU Frequencies for Single-Core Benchmark:
Before Benchmarks: 3.60 GHz
After Benchmarks: 3.60 GHz
==================================================
--- Multi-Threaded Benchmark ---
Setting Numba to use 8 thread(s)...
Warming up Numba-compiled functions...
Warm-up complete.
Retrieving CPU frequencies before benchmarks...
Average CPU Frequency (Before): 3.60 GHz
Running FP - add benchmark...
Add GOPS: 4.000 GOPS in 25.00 seconds
...
Benchmark Results:
Multi-Threaded-Type Operations:
Add: 4.000 GOPS, Estimated IPC: 1.11
Sub: 3.800 GOPS, Estimated IPC: 1.06
Mul: 3.600 GOPS, Estimated IPC: 1.00
Div: 2.400 GOPS, Estimated IPC: 0.67
CPU Frequencies for Multi-Threaded Benchmark:
Before Benchmarks: 3.60 GHz
After Benchmarks: 3.60 GHz
==================================================
```
### Notes and Best Practices
1. **Numba Caching**:
- Disabling caching between different thread configurations ensures that functions are recompiled with the correct number of threads.
- Alternatively, you can use separate function names or modules for different threading configurations to avoid conflicts.
2. **Environment Variables**:
- While this script sets the number of threads programmatically, you can also control Numba's threading behavior using environment variables like `NUMBA_NUM_THREADS` before running the script.
3. **Thread Safety**:
- Changing Numba's threading configuration at runtime is generally safe if done before any parallel Numba functions are called.
- Avoid changing thread settings while parallel operations are in progress.
4. **Performance Considerations**:
- Benchmark results can vary based on system load, CPU scaling, and thermal throttling. Ensure your system is in a consistent state for accurate benchmarking.
5. **Extensibility**:
- This script can be further extended to include more threading configurations or additional metrics based on your benchmarking needs.
By implementing these modifications, your benchmarking tool will now provide insightful metrics on both single-core and multi-threaded performance, allowing for a more comprehensive analysis of your CPU's capabilities.