Dheemanth aeab82ff30
CUDA 13.2 samples update (#432)
- Added Python samples for CUDA Python 1.0 release
- Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
2026-05-13 17:13:18 -05:00

249 lines
7.8 KiB
Python

# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
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"""
Process Checkpointing Sample using CUDA Core API.
The sample allocates a GPU buffer, fills it with a deterministic
pattern via a kernel, hashes the contents, runs the full
lock/checkpoint/restore/unlock cycle on its own PID, and re-hashes
the buffer afterwards to verify that the GPU memory contents
survived the round trip.
"""
import argparse
import hashlib
import os
import sys
import time
from dataclasses import dataclass
from typing import List
import numpy as np
from cuda.bindings import driver as cudrv
from cuda.core import (
Device,
LaunchConfig,
Program,
ProgramOptions,
checkpoint,
launch,
)
# Small fill kernel: deterministic, non-trivial pattern so the before/after
# hashes would disagree on any bit flip.
KERNEL_SRC = r"""
extern "C" __global__ void fill_pattern(float *out, unsigned long long n)
{
unsigned long long i = (unsigned long long)blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
float v = (float)(i & 0xFFFFu) * 1e-3f + 1.0f;
float u = (float)((i >> 16) & 0xFFFFu) * 1e-4f + 0.5f;
// A handful of dependent ops per element. Deterministic given i.
for (int k = 0; k < 8; ++k) {
v = v * 1.000001f + u;
u = u * 0.999999f + v * 1e-6f;
}
out[i] = v + u;
}
}
"""
@dataclass
class StepTiming:
label: str
duration_ms: float
state_after: str
def _cu_check(result) -> None:
err = result[0]
if int(err) != 0:
raise RuntimeError(f"CUDA driver call failed: {err}")
def compile_fill_kernel(device: Device):
options = ProgramOptions(std="c++17", arch=f"sm_{device.arch}")
program = Program(KERNEL_SRC, code_type="c++", options=options)
module = program.compile("cubin", name_expressions=("fill_pattern",))
return module.get_kernel("fill_pattern")
def hash_device_buffer(device_buffer, host: np.ndarray) -> str:
_cu_check(
cudrv.cuMemcpyDtoH(
host.ctypes.data,
device_buffer.handle,
host.nbytes,
)
)
return hashlib.sha256(host.tobytes()).hexdigest()[:16]
def _time_call(fn, *args, **kwargs) -> float:
t0 = time.monotonic()
fn(*args, **kwargs)
return (time.monotonic() - t0) * 1000.0
def run_lifecycle(proc: checkpoint.Process, lock_timeout_ms: int) -> List[StepTiming]:
"""
Drive the full `lock -> checkpoint -> restore -> unlock` cycle on
`proc` and return per-step timings with the state observed after
each step.
Note on state after `restore()`: the driver leaves the process in
the `locked` state. You must still call `unlock()` to return to
`running`.
"""
timings: List[StepTiming] = [StepTiming("initial", 0.0, proc.state)]
ms = _time_call(proc.lock, timeout_ms=lock_timeout_ms)
timings.append(StepTiming("lock", ms, proc.state))
ms = _time_call(proc.checkpoint)
timings.append(StepTiming("checkpoint", ms, proc.state))
ms = _time_call(proc.restore)
timings.append(StepTiming("restore", ms, proc.state))
ms = _time_call(proc.unlock)
timings.append(StepTiming("unlock", ms, proc.state))
return timings
def print_timings(timings: List[StepTiming]) -> None:
print()
header = f"{'step':<14}{'duration (ms)':>18}{'state after':>18}"
print(header)
print("-" * len(header))
total = 0.0
for t in timings:
if t.label == "initial":
dur = "-"
else:
dur = f"{t.duration_ms:.3f}"
total += t.duration_ms
print(f"{t.label:<14}{dur:>18}{t.state_after:>18}")
print("-" * len(header))
print(f"{'total':<14}{total:>18.3f}{'':>18}")
def main():
parser = argparse.ArgumentParser(
description="CUDA process checkpoint sample using cuda.core",
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument(
"--device", type=int, default=0, help="CUDA device ID (default: 0)"
)
parser.add_argument(
"--buffer-mib",
type=int,
default=16,
help="GPU buffer size in MiB (default: 16)",
)
parser.add_argument(
"--lock-timeout-ms",
type=int,
default=5000,
help="Timeout passed to Process.lock in ms (default: 5000)",
)
args = parser.parse_args()
if sys.platform != "linux":
print("Error: CUDA process checkpointing is Linux-only.")
return 1
if args.buffer_mib <= 0:
print("Error: --buffer-mib must be positive")
return 1
print("[Process Checkpoint Sample using CUDA Core API]")
print(f"PID: {os.getpid()}")
device = Device(args.device)
device.set_current()
print(f"Device: {device.name}")
print(f"Compute Capability: sm_{device.arch}")
print(f"Buffer size: {args.buffer_mib} MiB")
print(f"Lock timeout: {args.lock_timeout_ms} ms")
print()
print("Compiling kernel ...")
fill_kernel = compile_fill_kernel(device)
buffer_bytes = args.buffer_mib * 1024 * 1024
n_elements = buffer_bytes // 4 # float32
stream = device.create_stream()
device_buffer = device.memory_resource.allocate(buffer_bytes, stream=stream)
try:
print("Writing deterministic pattern to GPU buffer ...")
block = 256
grid = (n_elements + block - 1) // block
cfg = LaunchConfig(grid=grid, block=block)
launch(stream, cfg, fill_kernel, device_buffer, np.uint64(n_elements))
stream.sync()
host = np.empty(n_elements, dtype=np.float32)
hash_before = hash_device_buffer(device_buffer, host)
print(f"Buffer hash (before): {hash_before}")
print()
print("Running checkpoint lifecycle on self ...")
proc = checkpoint.Process(os.getpid())
timings = run_lifecycle(proc, args.lock_timeout_ms)
print_timings(timings)
hash_after = hash_device_buffer(device_buffer, host)
print()
print(f"Buffer hash (before): {hash_before}")
print(f"Buffer hash (after): {hash_after}")
if hash_before != hash_after:
print()
print("FAIL: GPU buffer contents changed across checkpoint/restore.")
return 1
print()
print("PASS: GPU buffer contents survived checkpoint/restore.")
finally:
device_buffer.close(stream)
print()
print("Done")
return 0
if __name__ == "__main__":
sys.exit(main())