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Improved power collection and added Xeon 4216 support

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This commit is contained in:
cephi 2024-12-11 22:47:11 -05:00
parent cbae1775bd
commit cf86de6ea4
8 changed files with 158 additions and 77 deletions
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25
analysis/data_stat.py
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@ -3,7 +3,7 @@ import re
from enum import Enum
class Stat(Enum):
PLATFORM = 'platform'
CPU = 'cpu'
THREADS = 'threads'
ITERATIONS = 'iterations'
@ -18,16 +18,20 @@ class Stat(Enum):
MATRIX_NNZ = 'matrix nnz'
MATRIX_DENSITY = 'matrix density %'
POWER_BEFORE = 'power before'
POWER = 'power'
POWER_AFTER = 'power after'
TIME_S_OVERALL = 'time (sec) overall'
#POWER_BEFORE = 'power before'
#POWER = 'power'
#POWER_AFTER = 'power after'
TIME_S = 'time (sec)'
JOULES_OVERALL = 'joules overall'
JOULES = 'joules'
WATTS_OVERALL = 'wattage overall'
WATTS = 'wattage'
DELTA_WATT = 'Δ watt'
TIME_S_1KI = 'time (sec) per 1k iterations'
J = 'joules'
J_1KI = 'joules per 1k iterations'
J_D = 'Δ joules'
J_D_1KI = 'Δ joules per 1k iterations'
W = 'watts'
W_1KI = 'watts per 1k iterations'
W_D = 'Δ watts'
W_D_1KI = 'Δ watts per 1k iterations'
#DELTA_WATT = 'Δ watt'
TASK_CLK = 'task clock (msec)'
PAGE_FAULTS = 'page faults'
@ -66,6 +70,7 @@ class Cpu(Enum):
#XEON = xeon_names
ALTRA = 'Altra'
EPYC_7313P = 'Epyc 7313P'
XEON_4216 = 'Xeon 4216'
names = {
Cpu.ALTRA: {

28
pytorch/power.sh
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@ -7,21 +7,33 @@ if [[ $arch = aarch64 ]]; then
iter=0
function aarch64_power {
sensors | awk '/CPU power:/ {print $3; exit}'
((iter++))
sleep 1s
}
baseline_time_s=-1
while [[ "$iter" -ne "$baseline_time_s" ]]; do
aarch64_power
done
if [[ "$baseline_time_s" -eq -1 ]]; then
#"${@:2}" 1>/dev/null 2>&1 &
"${@:2}" 1>&2 2>/dev/null &
pid=$!
while kill -0 $pid 2> /dev/null; do
aarch64_power
sleep 1s
done
wait
else
while [[ "$iter" -ne "$baseline_time_s" ]]; do
aarch64_power
((iter++))
sleep 1s
done
fi
elif [[ $arch = x86_64 ]]; then
if [[ "$baseline_time_s" -eq -1 ]]; then
#turbostat -s PkgWatt -i 1 2>/dev/null | awk -F: '/PkgWatt/ {getline; print $0}'
#turbostat -s PkgWatt -i 1 | awk '/PkgWatt/ {getline; print $0}'
turbostat -s PkgWatt ${@:2} | sed -n "/PkgWatt/{n;n;p}"
turbostat -s PkgWatt ${@:2} 3>&2 2>&1 1>&3 | sed -n "/PkgWatt/{n;n;p}"
else
#turbostat -s PkgWatt -n "$baseline_time_s" -i 1 2>/dev/null | awk -F: '/PkgWatt/ {getline; print $0}'
turbostat -s PkgWatt -n "$baseline_time_s" -i 1 | sed -n "/PkgWatt/{n;n;p}"
turbostat -s PkgWatt -n "$baseline_time_s" -i 1 2>/dev/null | sed -n "/PkgWatt/{n;n;p}"
fi
else
echo "Unrecognized arch!"

2
pytorch/pytorch-altra.Containerfile
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@ -3,7 +3,7 @@
FROM docker.io/amperecomputingai/pytorch:latest
RUN apt-get update -y \
&& apt-get install -y git vim wget \
&& apt-get install -y git vim wget htop \
&& rm -rf /var/lib/apt/lists/*
RUN pip install scipy

2
pytorch/pytorch-epyc_7313p.Containerfile
View File

@ -1,7 +1,7 @@
FROM --platform=linux/amd64 ubuntu:22.04
RUN apt-get update -y \
&& apt-get install -y python3 python3-pip git vim wget \
&& apt-get install -y python3 python3-pip git vim wget htop \
&& rm -rf /var/lib/apt/lists/*
RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
RUN pip install scipy

7
pytorch/pytorch-xeon_4216.Containerfile Normal file
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@ -0,0 +1,7 @@
FROM --platform=linux/amd64 ubuntu:22.04
RUN apt-get update -y \
&& apt-get install -y python3 python3-pip git vim wget htop numactl \
&& rm -rf /var/lib/apt/lists/*
RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
RUN pip install scipy

164
pytorch/run.py
View File

@ -13,7 +13,7 @@ parser.add_argument('matrix_file')
parser.add_argument('iterations', type=int)
parser.add_argument('baseline_time_s', type=int)
parser.add_argument('baseline_delay_s', type=int)
parser.add_argument('--perf', action='store_true')
#parser.add_argument('--perf', action='store_true')
parser.add_argument('--power', action='store_true')
parser.add_argument('-d', '--debug', action='store_true')
args = parser.parse_args()
@ -21,17 +21,22 @@ args.cpu = Cpu[args.cpu.upper()]
python = {
Cpu.ALTRA: 'python3',
Cpu.EPYC_7313P: 'python3.11'
}
program = {
Cpu.ALTRA: [
'apptainer', 'run', 'pytorch-altra.sif', '-c',
'numactl --cpunodebind=0 --membind=0 '
+ f'python spmv.py {args.matrix_file} {args.iterations}'],
Cpu.EPYC_7313P: [
'apptainer', 'run', 'pytorch-epyc_7313p.sif',
'python3', 'spmv.py', f'{args.matrix_file}', f'{args.iterations}']
Cpu.EPYC_7313P: 'python3.11',
Cpu.XEON_4216: 'python3.11'
}
#program = {
# Cpu.ALTRA: [
# 'apptainer', 'run', 'pytorch-altra.sif', '-c',
# 'numactl --cpunodebind=0 --membind=0 '
# + f'python3 spmv.py {args.matrix_file} {args.iterations}'],
# Cpu.EPYC_7313P: [
# 'apptainer', 'run', 'pytorch-epyc_7313p.sif',
# 'python3', 'spmv.py', f'{args.matrix_file}', f'{args.iterations}'],
# Cpu.XEON_4216: [
# 'apptainer', 'run', 'pytorch-altra.sif', '-c',
# 'numactl --cpunodebind=0 --membind=0 '
# + f'python3 spmv.py {args.matrix_file} {args.iterations}']
#}
perf = ['perf', 'stat']
perf_args = {
Cpu.ALTRA: [
@ -42,8 +47,24 @@ perf_args = {
['-M', 'l2_cache_miss_ratio,l2_tlb_miss_ratio,ll_cache_read_miss_ratio']]
}
def program(cpu: Cpu, matrix_file: str, iterations: int) -> list:
if cpu == Cpu.ALTRA:
return [
'apptainer', 'run', 'pytorch-altra.sif', '-c',
'numactl --cpunodebind=0 --membind=0 '
+ f'python3 spmv.py {matrix_file} {iterations}']
elif cpu == Cpu.EPYC_7313P:
return [
'apptainer', 'run', 'pytorch-epyc_7313p.sif',
'python3', 'spmv.py', f'{matrix_file}', f'{iterations}']
elif cpu == Cpu.XEON_4216:
return [
'apptainer', 'run', 'pytorch-xeon_4216.sif',
'numactl', '--cpunodebind=0', '--membind=0',
'python3', 'spmv.py', f'{matrix_file}', f'{iterations}']
def baseline_power(cpu: Cpu, baseline_time_s: int) -> list:
power_process = subprocess.Popen([python[args.cpu], 'power.py', '-s', str(baseline_time_s)],
power_process = subprocess.Popen(['./power.sh', str(baseline_time_s)],
stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
return [float(x) for x in power_process.communicate()[0].strip().split('\n') if len(x) != 0]
@ -52,18 +73,22 @@ def run_program(program: list[str]) -> tuple[dict, str]:
print(program)
process = subprocess.run(program,
stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
process.check_returncode()
if args.debug:
print(process.stdout)
print(process.stderr)
return (json.loads(process.stdout), process.stderr)
def trapezoidal_rule(power: list[float], time_s: float) -> float:
from math import ceil
from itertools import pairwise
result = 0.0
relevant_power = power[-int(time_s):]
relevant_pairs = [pair for pair in zip(relevant_power, relevant_power[1:])]
for pair in relevant_pairs:
assert(time_s >= 2)
assert(len(relevant_power) >= 2)
assert(len(power) >= time_s)
for pair in zip(relevant_power, relevant_power[1:]):
result += 0.5 * (pair[0] + pair[1])
result += (time_s % 1) * (power[-1])
return result
@ -72,74 +97,105 @@ result = dict()
result[Stat.CPU.name] = args.cpu.name
result[Stat.ITERATIONS.name] = args.iterations
program_result = run_program(program[args.cpu])
program_result = run_program(program(args.cpu, args.matrix_file, args.iterations))
result |= program_result[0]
print(program_result[1], file=sys.stderr)
result[Stat.TIME_S.name] = result[Stat.TIME_S_OVERALL.name] / result[Stat.ITERATIONS.name]
result[Stat.TIME_S_1KI.name] = (
(result[Stat.TIME_S.name] / result[Stat.ITERATIONS.name]) * 1000
)
if args.power:
result[Stat.BASELINE_TIME_S.name] = args.baseline_time_s
result[Stat.BASELINE_DELAY_S.name] = args.baseline_delay_s
# Baseline
time.sleep(args.baseline_delay_s)
result[Stat.POWER_BEFORE.name] = baseline_power(args.cpu, args.baseline_time_s)
#result[Stat.POWER_BEFORE.name] = baseline_power(args.cpu, args.baseline_time_s)
baseline_list = baseline_power(args.cpu, args.baseline_time_s)
if args.debug:
print(result)
print(baseline_list)
assert(len(baseline_list) == args.baseline_time_s)
run_program(program[args.cpu]) # Warmup
# Power Collection
power_process = subprocess.run(
['./power.sh', '-1'] + program(args.cpu, args.matrix_file, args.iterations),
stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
power_process.check_returncode()
#result[Stat.POWER.name] = [float(x) for x in power_process.communicate()[0].strip().split('\n')]
power_list = [float(x)
#for x in power_process.communicate()[0].strip().split('\n')]
for x in power_process.stdout.strip().split('\n')]
power_process_time_s = json.loads(power_process.stderr)[Stat.TIME_S.name]
if args.debug:
print(power_list)
print(power_process_time_s)
if args.cpu == Cpu.ALTRA:
power_process = subprocess.Popen(
[python[args.cpu], 'power.py'],
stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
run_program(program[args.cpu])[0]
power_process.send_signal(signal.SIGINT)
if args.debug:
print(result)
result[Stat.POWER.name] = [float(x) for x in power_process.communicate()[0].strip().split('\n')]
# Trapezoidal Rule across the last (s) power recordings.
#result[Stat.JOULES.name] = (
# sum(result[Stat.POWER.name][-ceil(result[Stat.TIME_S.name]):])
# + (result[Stat.POWER.name][-1] * (result[Stat.TIME_S.name] % 1)))
result[Stat.JOULES.name] = trapezoidal_rule(
result[Stat.POWER.name],
result[Stat.TIME_S.name])
result[Stat.J.name] = trapezoidal_rule(
power_list, power_process_time_s)
elif args.cpu == Cpu.EPYC_7313P or args.cpu == Cpu.XEON_4216:
result[Stat.J.name] = power_list[0] * power_process_time_s
elif args.cpu == Cpu.EPYC_7313P:
power_process = subprocess.Popen(
[python[args.cpu], 'power.py'] + program[args.cpu],
stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
result[Stat.POWER.name] = [float(x) for x in power_process.communicate()[0].strip().split('\n')]
result[Stat.JOULES.name] = result[Stat.POWER.name][0] * result[Stat.TIME_S_OVERALL.name]
result[Stat.W.name] = result[Stat.J.name] / power_process_time_s
if args.debug:
print(result)
#print(len(result['power']))
#print(sum(result['power']) / len(result['power']))
# Baseline
time.sleep(args.baseline_delay_s)
result[Stat.POWER_AFTER.name] = baseline_power(args.cpu, args.baseline_time_s)
#result[Stat.POWER_AFTER.name] = baseline_power(args.cpu, args.baseline_time_s)
baseline_list += baseline_power(args.cpu, args.baseline_time_s)
if args.debug:
print(result)
print(baseline_list)
assert(len(baseline_list) / 2 == args.baseline_time_s)
if args.perf:
for perf_arg in perf_args[args.cpu]:
output = run_program(perf + perf_arg + program[args.cpu])[1]
print(output, file=sys.stderr)
result = result | data_stat.parse_output(output, args.cpu)
if args.debug:
print(result)
baseline_joules = (
trapezoidal_rule(
baseline_list[:args.baseline_time_s],
args.baseline_time_s) +
trapezoidal_rule(
baseline_list[args.baseline_time_s:],
args.baseline_time_s)
)
baseline_wattage = baseline_joules / (args.baseline_time_s * 2)
result = result | data_stat.derive_stats(result)
result[Stat.J_1KI.name] = (
(result[Stat.J.name] / result[Stat.ITERATIONS.name]) * 1000
)
result[Stat.W_1KI.name] = (
(result[Stat.W.name] / result[Stat.ITERATIONS.name]) * 1000
)
result[Stat.W_D.name] = result[Stat.W.name] - baseline_wattage
result[Stat.J_D.name] = result[Stat.W_D.name] * power_process_time_s
result[Stat.W_D_1KI.name] = (
(result[Stat.W_D.name] / result[Stat.ITERATIONS.name]) * 1000
)
result[Stat.J_D_1KI.name] = (
(result[Stat.W_D_1KI.name] / result[Stat.ITERATIONS.name]) * 1000
)
if args.debug:
print(result)
print(json.dumps(result))
#if args.perf:
# for perf_arg in perf_args[args.cpu]:
# output = run_program(perf + perf_arg + program[args.cpu])[1]
# print(output, file=sys.stderr)
# result = result | data_stat.parse_output(output, args.cpu)
# if args.debug:
# print(result)
#
# result = result | data_stat.derive_stats(result)
#
# if args.debug:
# print(result)
#arch = subprocess.run(['uname', '-m'], stdout=subprocess.PIPE, text=True).stdout.strip()
#baseline = subprocess.run(

4
pytorch/spmv.py
View File

@ -50,7 +50,7 @@ print(f"NNZ: {result[Stat.MATRIX_NNZ.name]}", file=sys.stderr)
result[Stat.MATRIX_DENSITY.name] = matrix.values().shape[0] / (matrix.shape[0] * matrix.shape[1])
print(f"Density: {result[Stat.MATRIX_DENSITY.name]}", file=sys.stderr)
result[Stat.TIME_S_OVERALL.name] = end - start
print(f"Time: {result[Stat.TIME_S_OVERALL.name]} seconds", file=sys.stderr)
result[Stat.TIME_S.name] = end - start
print(f"Time: {result[Stat.TIME_S.name]} seconds", file=sys.stderr)
print(json.dumps(result))

1
pytorch/tmp.txt Normal file
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@ -0,0 +1 @@
{"MATRIX_FILE": "as-caida", "MATRIX_SHAPE": [31379, 31379], "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106762, "MATRIX_DENSITY": 0.00010842726485909405, "TIME_S_T": 2.6111819744110107}