improve data_stat

analysis/data_stat.py

@@ -5,13 +5,21 @@ from enum import Enum
 class Stat(Enum):
     CPU = 'cpu'
 
+    ITERATIONS = 'iterations'
+    BASELINE_TIME_S = 'baseline time (sec)'
+    BASELINE_DELAY_S = 'baseline delay (sec)'
+
     SOLVER = 'solver'
-    LIN_ALG = 'linear algebra'
+
-    INPUT_FILE = 'input file'
+    MATRIX_FILE = 'matrix file'
-    MAXWELL_SIZE = 'maxwell size'
+    MATRIX_SHAPE = 'matrix shape'
-    MATRIX_COLS = 'matrix columns'
+    MATRIX_SIZE = 'matrix size'
+    MATRIX_NNZ = 'matrix nnz'
+    MATRIX_DENSITY = 'matrix density %'
+    TIME_S = 'time (sec)'
 
     POWER_DELTA = 'Δ watt'
+    JOULES = 'joules'
 
     TASK_CLK = 'task clock (msec)'
     PAGE_FAULTS = 'page faults'
@@ -45,7 +53,14 @@ class Stat(Enum):
     L2D_CACHE_MISS_RATE = 'L2D cache miss rate'
     LL_CACHE_MISS_RATE = 'LL cache miss rate'
 
-altra_names = {
+class Cpu(Enum):
+    #ALTRA = altra_names
+    #XEON = xeon_names
+    ALTRA = 'Altra'
+    EPYC_7313P = 'Epyc 7313P'
+
+names = {
+        Cpu.ALTRA: {
             Stat.TASK_CLK: 'task-clock:u',
             Stat.PAGE_FAULTS: 'page-faults:u',
             Stat.CYCLES: 'cycles:u',
@@ -67,9 +82,8 @@ altra_names = {
             Stat.L2D_CACHE_MISS: 'L2D_CACHE_REFILL:u',
             Stat.LL_CACHE: 'LL_CACHE_RD:u',
             Stat.LL_CACHE_MISS: 'LL_CACHE_MISS_RD:u',
-}
+        },
-
+        Cpu.EPYC_7313P: {
-xeon_names = {
             Stat.TASK_CLK: 'task-clock:u',
             Stat.PAGE_FAULTS: 'page-faults:u',
             Stat.CYCLES: 'cycles:u',
@@ -88,10 +102,7 @@ xeon_names = {
             Stat.LL_CACHE: 'LLC-loads:u',
             Stat.LL_CACHE_MISS: 'LLC-load-misses:u',
         }
-
+}
-class CPU(Enum):
-    ALTRA = altra_names
-    XEON = xeon_names
 
 def parse_output_old(filename: str, data: dict[str, str]) -> dict:
     result: dict[str, int | float] = dict()
@@ -112,12 +123,12 @@ def parse_output_old(filename: str, data: dict[str, str]) -> dict:
 
     return result | parse_power(filename, cpu)
 
-def parse_output(output: str, cpu: CPU) -> dict:
+def parse_output(output: str, cpu: Cpu) -> dict:
     result = dict()
 
     for line in output.split('\n'):
-        for stat in [x for x in Stat if x in cpu.value]:
+        for stat in [x for x in Stat if x in names[cpu]]:
-            regex = r'^\W*([\d+(,|\.)?]+)\W*.*' + cpu.value[stat]
+            regex = r'^\W*([\d+(,|\.)?]+)\W*.*' + names[cpu][stat]
             value = re.search(regex, line)
 
             if value is None:

pytorch/data_stat.py

@@ -0,0 +1 @@
+../analysis/data_stat.py

pytorch/perf_stat.py

@@ -1 +0,0 @@
-../analysis/perf_stat.py

pytorch/run.py

@@ -1,14 +1,16 @@
 #! /bin/python3
 
-import perf_stat
+import data_stat
+from data_stat import Stat, Cpu
 
 import argparse
 import os, sys
 import subprocess, signal
 import json
+import time
 
 parser = argparse.ArgumentParser()
-parser.add_argument('arch')
+parser.add_argument('cpu', choices=[x.name.lower() for x in Cpu])
 parser.add_argument('matrix_file')
 parser.add_argument('iterations', type=int)
 parser.add_argument('baseline_time_s', type=int)
@@ -17,17 +19,23 @@ 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()
+args.cpu = Cpu[args.cpu.upper()]
 
-program_altra = [
+program = {
+        Cpu.ALTRA: [
             'apptainer', 'run', 'pytorch-altra.sif', '-c',
             'numactl --cpunodebind=0 --membind=0 '
             + f'python spmv.py {args.matrix_file} {args.iterations}']
+}
 perf = ['perf', 'stat']
-perf_altra = [['-d', '-d'],
+perf_args = {
+        Cpu.ALTRA: [
+            ['-d', '-d'],
             ['-M', 'branch_misprediction_ratio'],
             ['-M', 'dtlb_walk_ratio,itlb_walk_ratio'],
             ['-M', 'l1d_cache_miss_ratio,l1i_cache_miss_ratio'],
             ['-M', 'l2_cache_miss_ratio,l2_tlb_miss_ratio,ll_cache_read_miss_ratio']]
+}
 
 def baseline_power(baseline_time_s: int) -> list:
     power_process = subprocess.Popen(['./power.py', '-s', str(baseline_time_s)],
@@ -37,33 +45,29 @@ def baseline_power(baseline_time_s: int) -> list:
 def run_program(program: list[str]) -> tuple[dict, str]:
     process = subprocess.run(program,
             stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-    #print(json.loads(process.stdout))
+    if args.debug:
-    #print(process.stderr)
+        print(process.stdout)
+        print(process.stderr)
     return (json.loads(process.stdout), process.stderr)
 
 result = dict()
-result['architecture'] = args.arch
+result[Stat.CPU.value] = args.cpu.value
-result['iterations'] = args.iterations
+result[Stat.ITERATIONS.value] = args.iterations
-result['baseline_time_s'] = args.baseline_time_s
+result[Stat.BASELINE_TIME_S.value] = args.baseline_time_s
-result['baseline_delay_s'] = args.baseline_delay_s
+result[Stat.BASELINE_DELAY_S.value] = args.baseline_delay_s
 
-if args.power is True:
+if args.power:
+    time.sleep(args.baseline_delay_s)
     result['power_before'] = baseline_power(args.baseline_time_s)
     if args.debug:
         print(result)
 
-    run_program(program_altra) # Warmup
+    print(program[args.cpu])
+    run_program(program[args.cpu]) # Warmup
     power_process = subprocess.Popen(['./power.py'],
             stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
 
-    if args.arch == 'altra':
+    result = result | run_program(program[args.cpu])[0]
-        result = result | run_program(program_altra)[0]
-    elif args.arch == 'x86':
-        print("Arch not implemented yet!")
-        exit(1)
-    else:
-        print("Unrecognized arch!")
-        exit(1)
 
     power_process.send_signal(signal.SIGINT)
     if args.debug:
@@ -75,28 +79,20 @@ if args.power is True:
         #print(len(result['power']))
         #print(sum(result['power']) / len(result['power']))
 
-    import time
     time.sleep(args.baseline_delay_s)
     result['power_after'] = baseline_power(args.baseline_time_s)
     if args.debug:
         print(result)
 
-if args.perf is True:
+if args.perf:
-    if args.arch == 'altra':
+    for perf_arg in perf_args[args.cpu]:
-        for perf_args in perf_altra:
+        output = run_program(perf + perf_arg + program[args.cpu])[1]
-            output = run_program(perf + perf_args + program_altra)[1]
         print(output, file=sys.stderr)
-            result = result | perf_stat.parse_output(output, perf_stat.CPU.ALTRA)
+        result = result | data_stat.parse_output(output, args.cpu)
         if args.debug:
             print(result)
-    elif args.arch == 'x86':
-        print("no implement")
-        exit(1)
-    else:
-        print("Unrecognized arch!")
-        exit(1)
 
-    result = result | perf_stat.derive_stats(result)
+    result = result | data_stat.derive_stats(result)
 
     if args.debug:
         print(result)

pytorch/spmv.py

@@ -1,3 +1,5 @@
+from data_stat import Stat
+
 import torch, scipy
 import numpy as np
 import argparse
@@ -32,19 +34,22 @@ end = time.time()
 
 result = dict()
 
-result['matrix'] = os.path.splitext(os.path.basename(args.matrix_file))[0]
+result[Stat.MATRIX_FILE.value] = os.path.splitext(os.path.basename(args.matrix_file))[0]
-print(f"Matrix: {result['matrix']}", file=sys.stderr)
+print(f"Matrix: {result[Stat.MATRIX_FILE.value]}", file=sys.stderr)
 
-result['shape'] = matrix.shape
+result[Stat.MATRIX_SHAPE.value] = matrix.shape
-print(f"Shape: {result['shape']}", file=sys.stderr)
+print(f"Shape: {result[Stat.MATRIX_SHAPE.value]}", file=sys.stderr)
 
-result['nnz'] = matrix.values().shape[0]
+result[Stat.MATRIX_SIZE.value] = matrix.shape[0] * matrix.shape[1]
-print(f"NNZ: {result['nnz']}", file=sys.stderr)
+print(f"Size: {result[Stat.MATRIX_SIZE.value]}", file=sys.stderr)
 
-result['% density'] = matrix.values().shape[0] / (matrix.shape[0] * matrix.shape[1])
+result[Stat.MATRIX_NNZ.value] = matrix.values().shape[0]
-print(f"Density: {result['% density']}", file=sys.stderr)
+print(f"NNZ: {result[Stat.MATRIX_NNZ.value]}", file=sys.stderr)
 
-result['time_s'] = end - start
+result[Stat.MATRIX_DENSITY.value] = matrix.values().shape[0] / (matrix.shape[0] * matrix.shape[1])
-print(f"Time: {result['time_s']} seconds", file=sys.stderr)
+print(f"Density: {result[Stat.MATRIX_DENSITY.value]}", file=sys.stderr)
+
+result[Stat.TIME_S.value] = end - start
+print(f"Time: {result[Stat.TIME_S.value]} seconds", file=sys.stderr)
 
 print(json.dumps(result))