analysis/data_stat.py

@@ -3,7 +3,7 @@ import re
 from enum import Enum
 
 class Stat(Enum):
-    CPU = 'cpu'
+    PLATFORM = 'platform'
 
     THREADS = 'threads'
     ITERATIONS = 'iterations'
@@ -18,20 +18,16 @@ class Stat(Enum):
     MATRIX_NNZ = 'matrix nnz'
     MATRIX_DENSITY = 'matrix density %'
  
-    #POWER_BEFORE = 'power before'
+    POWER_BEFORE = 'power before'
-    #POWER = 'power'
+    POWER = 'power'
-    #POWER_AFTER = 'power after'
+    POWER_AFTER = 'power after'
+    TIME_S_OVERALL = 'time (sec) overall'
     TIME_S = 'time (sec)'
-    TIME_S_1KI = 'time (sec) per 1k iterations'
+    JOULES_OVERALL = 'joules overall'
-    J = 'joules'
+    JOULES = 'joules'
-    J_1KI = 'joules per 1k iterations'
+    WATTS_OVERALL = 'wattage overall'
-    J_D = 'Δ joules'
+    WATTS = 'wattage'
-    J_D_1KI = 'Δ joules per 1k iterations'
+    DELTA_WATT = 'Δ watt'
-    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'
@@ -70,7 +66,6 @@ class Cpu(Enum):
     #XEON = xeon_names
     ALTRA = 'Altra'
     EPYC_7313P = 'Epyc 7313P'
-    XEON_4216 = 'Xeon 4216'
 
 names = {
         Cpu.ALTRA: {

pytorch/power.sh

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

pytorch/pytorch-altra.Containerfile

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

pytorch/pytorch-epyc_7313p.Containerfile

@@ -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 htop \
+	&& apt-get install -y python3 python3-pip git vim wget \
 	&& rm -rf /var/lib/apt/lists/*
 RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
 RUN pip install scipy

pytorch/pytorch-xeon_4216.Containerfile

@@ -1,7 +0,0 @@
-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

pytorch/run.py

@@ -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()
@@ -21,22 +21,17 @@ args.cpu = Cpu[args.cpu.upper()]
 
 python = {
         Cpu.ALTRA: 'python3',
-        Cpu.EPYC_7313P: 'python3.11',
+        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'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}']
 }
-#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: [
@@ -47,24 +42,8 @@ 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(['./power.sh', str(baseline_time_s)],
+    power_process = subprocess.Popen([python[args.cpu], 'power.py', '-s', 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]
 
@@ -73,129 +52,81 @@ 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:
-    result = 0.0
-    relevant_power = power[-int(time_s):]
-
-    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
-
 result = dict()
 result[Stat.CPU.name] = args.cpu.name
 result[Stat.ITERATIONS.name] = args.iterations
 
-program_result = run_program(program(args.cpu, args.matrix_file, args.iterations))
+program_result = run_program(program[args.cpu])
 result |= program_result[0]
 print(program_result[1], file=sys.stderr)
 
-result[Stat.TIME_S_1KI.name] = (
+result[Stat.TIME_S.name] = result[Stat.TIME_S_OVERALL.name] / result[Stat.ITERATIONS.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(baseline_list)
+        print(result)
-    assert(len(baseline_list) == args.baseline_time_s)
 
-    # Power Collection
+    run_program(program[args.cpu]) # Warmup
-    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:
-        # Trapezoidal Rule across the last (s) power recordings.
+        power_process = subprocess.Popen(
-        result[Stat.J.name] = trapezoidal_rule(
+                [python[args.cpu], 'power.py'],
-                power_list, power_process_time_s)
+                stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
-    elif args.cpu == Cpu.EPYC_7313P or args.cpu == Cpu.XEON_4216:
-        result[Stat.J.name] = power_list[0] * power_process_time_s 
 
-    result[Stat.W.name] = result[Stat.J.name] / power_process_time_s
+        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')]
+        # Riemann Sum across the last (s) power recordings.
+        from math import ceil
+        result[Stat.JOULES.name] = (
+                sum(result[Stat.POWER.name][-ceil(result[Stat.TIME_S_OVERALL.name]):-1])
+                + (result[Stat.POWER.name][-1] * (result[Stat.TIME_S_OVERALL.name] % 1)))
+
+    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]
 
     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(baseline_list)
+        print(result)
-    assert(len(baseline_list) / 2 == args.baseline_time_s)
 
-    baseline_joules = (
+if args.perf:
-        trapezoidal_rule(
+    for perf_arg in perf_args[args.cpu]:
-            baseline_list[:args.baseline_time_s],
+        output = run_program(perf + perf_arg + program[args.cpu])[1]
-            args.baseline_time_s) +
+        print(output, file=sys.stderr)
-        trapezoidal_rule(
+        result = result | data_stat.parse_output(output, args.cpu)
-            baseline_list[args.baseline_time_s:],
+        if args.debug:
-            args.baseline_time_s)
+            print(result)
-    )
-    baseline_wattage = baseline_joules / (args.baseline_time_s * 2)
 
-    result[Stat.J_1KI.name] = (
+    result = result | data_stat.derive_stats(result)
-            (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(

pytorch/spmv.py

@@ -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.name] = end - start
+result[Stat.TIME_S_OVERALL.name] = end - start
-print(f"Time: {result[Stat.TIME_S.name]} seconds", file=sys.stderr)
+print(f"Time: {result[Stat.TIME_S_OVERALL.name]} seconds", file=sys.stderr)
 
 print(json.dumps(result))

pytorch/tmp.txt

@@ -1 +0,0 @@
-{"MATRIX_FILE": "as-caida", "MATRIX_SHAPE": [31379, 31379], "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106762, "MATRIX_DENSITY": 0.00010842726485909405, "TIME_S_T": 2.6111819744110107}