Fixed some bugs

analysis/data_stat.py

@@ -5,7 +5,7 @@ from enum import Enum
 class Stat(Enum):
     CPU = 'cpu'
 
-    THREADS = 'threads'
+    CORES = 'cores'
     ITERATIONS = 'iterations'
     BASELINE_TIME_S = 'baseline time (sec)'
     BASELINE_DELAY_S = 'baseline delay (sec)'
@@ -16,8 +16,10 @@ class Stat(Enum):
     MATRIX_FILE = 'matrix file'
     MATRIX_FORMAT = 'matrix format'
     MATRIX_SHAPE = 'matrix shape'
+    MATRIX_ROWS = 'matrix rows'
     MATRIX_SIZE = 'matrix size'
     MATRIX_NNZ = 'matrix nnz'
+    MATRIX_DENSITY_GROUP = 'matrix density group'
     MATRIX_DENSITY = 'matrix density'
  
     #POWER_BEFORE = 'power before'

pytorch/batch.py

@@ -83,7 +83,8 @@ def run(
             str(args.base_iterations),
             str(args.min_time_s),
             str(args.baseline_time_s),
-            str(args.baseline_delay_s)]
+            str(args.baseline_delay_s),
+            '-d']
 
     if args.matrix_type == MatrixType.SUITESPARSE:
         run_args_list += ['-m', matrix_file]
@@ -95,7 +96,7 @@ def run(
 #        run_args_list += [args.perf]
 
     if args.cores is not None:
-        run_args_list += ['-c', args.cores]
+        run_args_list += ['-c', str(args.cores)]
 
     if args.power is not None:
         run_args_list += [args.power]
@@ -115,7 +116,8 @@ if args.matrix_type == MatrixType.SUITESPARSE:
 elif args.matrix_type == MatrixType.SYNTHETIC:
     parameter_list = enumerate([(size, density)
         for size in args.synthetic_size
-        for density in args.synthetic_density])
+        for density in args.synthetic_density
+        if size ** 2 * density < 100000000])
 
 #for i, matrix in enumerate(glob.glob(f'{args.matrix_dir.rstrip("/")}/*.mtx')):
 for i, parameter in parameter_list:

pytorch/run.py

@@ -60,7 +60,6 @@ def program(
     if cores is not None:
         apptainer += [ '--env', 'OMP_PROC_BIND=true',
             '--env', 'OMP_PLACES={0:' + f'{cores}' + '}']
-    apptainer = ['apptainer', 'run']
 
     spmv = f'python3 spmv.py {matrix_type.name.lower()} {fmt.name.lower()} '
     spmv += f'{iterations} '
@@ -70,6 +69,7 @@ def program(
         spmv += f'-ss {synthetic_size} -sd {synthetic_density}'
     else:
         exit("Unrecognized matrix type!")
+
     if cores is not None:
         spmv += f' -c {cores}'
 
@@ -114,6 +114,15 @@ def trapezoidal_rule(power: list[float], time_s: float) -> float:
 
 result = dict()
 result[Stat.CPU.name] = args.cpu.value
+if args.cores is not None:
+    result[Stat.CORES.name] = args.cores
+else:
+    if args.cpu == Cpu.ALTRA:
+        result[Stat.CORES.name] = 80
+    elif args.cpu == Cpu.EPYC_7313P:
+        result[Stat.CORES.name] = 16
+    elif args.cpu == Cpu.XEON_4216:
+        result[Stat.CORES.name] = 16
 
 iterations = args.base_iterations
 program_result = run_program(program(

pytorch/spmv.py

@@ -80,11 +80,13 @@ end = time.time()
 result = dict()
 
 result[Stat.MATRIX_TYPE.name] = args.matrix_type.value
-print(f"Matrix: {result[Stat.MATRIX_TYPE.name]}", file=sys.stderr)
+print(f"Matrix Type: {result[Stat.MATRIX_TYPE.name]}", file=sys.stderr)
 
 if args.matrix_type == MatrixType.SUITESPARSE:
     result[Stat.MATRIX_FILE.name] = os.path.splitext(os.path.basename(args.matrix_file))[0]
     print(f"Matrix: {result[Stat.MATRIX_FILE.name]}", file=sys.stderr)
+elif args.matrix_type == MatrixType.SYNTHETIC:
+    result[Stat.MATRIX_DENSITY_GROUP.name] = args.synthetic_density
 
 result[Stat.MATRIX_FORMAT.name] = args.format.value
 print(f"Matrix: {result[Stat.MATRIX_FORMAT.name]}", file=sys.stderr)
@@ -92,6 +94,9 @@ print(f"Matrix: {result[Stat.MATRIX_FORMAT.name]}", file=sys.stderr)
 result[Stat.MATRIX_SHAPE.name] = matrix.shape
 print(f"Shape: {result[Stat.MATRIX_SHAPE.name]}", file=sys.stderr)
 
+result[Stat.MATRIX_ROWS.name] = matrix.shape[0]
+print(f"Rows: {result[Stat.MATRIX_ROWS.name]}", file=sys.stderr)
+
 result[Stat.MATRIX_SIZE.name] = matrix.shape[0] * matrix.shape[1]
 print(f"Size: {result[Stat.MATRIX_SIZE.name]}", file=sys.stderr)