Merge branch 'database_rewrite' into development

.gitignore

@@ -2,5 +2,6 @@ build
 stash
 .*
 *.pyc
-*.db
+database.json
+database_best.json
 cl.hpp

scripts/database/database.py

@@ -11,8 +11,6 @@ import os.path
 import glob
 import argparse
 
-import pandas as pd
-
 import database.io as io
 import database.db as db
 import database.clblast as clblast
@@ -20,15 +18,15 @@ import database.bests as bests
 import database.defaults as defaults
 
 # Server storing a copy of the database
-DATABASE_SERVER_URL = "http://www.cedricnugteren.nl/tuning/clblast.db"
+DATABASE_SERVER_URL = "http://www.cedricnugteren.nl/tuning/clblast.json"
 
 # OpenCL vendor names and their short name
-VENDOR_TRANSLATION_TABLE = {"device_vendor": {
+VENDOR_TRANSLATION_TABLE = {
   "GenuineIntel": "Intel",
   "Intel(R) Corporation": "Intel",
   "Advanced Micro Devices, Inc.": "AMD",
   "NVIDIA Corporation": "NVIDIA",
-}}
+}
 
 
 def main(argv):
@@ -41,7 +39,8 @@ def main(argv):
     cl_args = parser.parse_args(argv)
 
     # Parses the path arguments
-    database_filename = os.path.join(cl_args.clblast_root, "scripts", "database", "database.db")
+    database_filename = os.path.join(cl_args.clblast_root, "scripts", "database", "database.json")
+    database_best_filename = os.path.join(cl_args.clblast_root, "scripts", "database", "database_best.json")
     json_files = os.path.join(cl_args.source_folder, "*.json")
     cpp_database_path = os.path.join(cl_args.clblast_root, "src", "database", "kernels")
 
@@ -52,11 +51,6 @@ def main(argv):
     if len(glob.glob(json_files)) < 1:
         print("[database] The path '" + cl_args.source_folder + "' does not contain any JSON files")
 
-    # Pandas options
-    pd.set_option('display.width', 1000)
-    if cl_args.verbose:
-        print("[database] Using pandas version " + pd.__version__)
-
     # Downloads the database if a local copy is not present
     if not os.path.isfile(database_filename):
         io.download_database(database_filename, DATABASE_SERVER_URL)
@@ -68,30 +62,22 @@ def main(argv):
     for file_json in glob.glob(json_files):
 
         # Loads the newly imported data
-        sys.stdout.write("[database] Processing '"+file_json+"' ")  # No newline printed
+        sys.stdout.write("[database] Processing '" + file_json + "' ")  # No newline printed
-        imported_data = io.load_json_to_pandas(file_json)
+        imported_data = io.load_tuning_results(file_json)
 
         # Fixes the problem that some vendors use multiple different names
-        imported_data = db.find_and_replace(imported_data, VENDOR_TRANSLATION_TABLE)
+        for target in VENDOR_TRANSLATION_TABLE:
+            if imported_data["device_vendor"] == target:
+                imported_data["device_vendor"] = VENDOR_TRANSLATION_TABLE[target]
 
         # Adds the new data to the database
-        old_size = len(database.index)
+        old_size = db.length(database)
-        database = db.concatenate_database(database, imported_data)
+        database = db.add_section(database, imported_data)
-        database = database.drop_duplicates()
+        new_size = db.length(database)
-        new_size = len(database.index)
         print("with " + str(new_size - old_size) + " new items")  # Newline printed here
 
     # Stores the modified database back to disk
     if len(glob.glob(json_files)) >= 1:
-        database = db.remove_duplicates(database)
-        io.save_database(database, database_filename)
-
-    # Optional: update the database here. Default is disabled, code below is just an example
-    if False:  # TODO: Use command-line arguments to enable updates in a flexible way
-        database = db.update_database(database,
-                                      ((database["kernel"] == "CopyMatrixFast") &
-                                       (database["precision"] == "3232")),
-                                      "arg_alpha", "2+0.5i")
         io.save_database(database, database_filename)
 
     # Retrieves the best performing results
@@ -101,7 +87,11 @@ def main(argv):
     # Determines the defaults for other vendors and per vendor
     print("[database] Calculating the default values...")
     database_defaults = defaults.calculate_defaults(database, cl_args.verbose)
-    database_best_results = db.concatenate_database(database_best_results, database_defaults)
+    database_best_results["sections"].extend(database_defaults["sections"])
+
+    # Optionally outputs the database to disk
+    if cl_args.verbose:
+        io.save_database(database_best_results, database_best_filename)
 
     # Outputs the database as a C++ database
     print("[database] Producing a C++ database in '" + cpp_database_path + "'...")

scripts/database/database/bests.py

@@ -5,39 +5,54 @@
 # Author(s):
 #   Cedric Nugteren <www.cedricnugteren.nl>
 
-import pandas as pd
+import sys
-import clblast
 
 
-def get_best_results(df):
+def get_best_results(database):
-    """Retrieves the results with the lowests execution times"""
+    """Retrieves the results with the lowest execution times"""
-    database_bests = pd.DataFrame()
+    sections_best = []
-    database_entries = df.groupby(clblast.ATTRIBUTES + ["kernel"])
+    for section in database["sections"]:
-    for name, database_entry in database_entries:
+        section_best = {}
-        best_time = database_entry["time"].min()
+
-        best_parameters = database_entry[database_entry["time"] == best_time].iloc[0]
+        # Stores all the section's meta data
-        database_bests = database_bests.append(best_parameters, ignore_index=True)
+        for attribute in section.keys():
-    return database_bests
+            if attribute != "results":
+                section_best[attribute] = section[attribute]
+
+        # Find the best result
+        parameters_best = None
+        time_best = sys.float_info.max
+        for result in section["results"]:
+            if result["time"] < time_best:
+                time_best = result["time"]
+                parameters_best = result["parameters"]
+
+        # Stores the best result
+        section_best["results"] = [{"time": time_best, "parameters": parameters_best}]
+        sections_best.append(section_best)
+
+    return {"sections": sections_best}
 
 
-def get_relative_bests(df, parameter_column_names, name, verbose=False):
+def get_relative_bests(name, common_results, common_parameters, verbose=False):
-    """Retrieves the relative best execution time over different devices"""
+    """Retrieves the parameters with the relative best execution time over different devices"""
+
+    # Helper function
+    def argmax(iterable):
+        return max(enumerate(iterable), key=lambda x: x[1])[0]
 
     # Computes the sum of the execution times over the different devices
-    def sum_performance(x):
+    performance_sums = []
-        x["group_performance"] = x["relative_performance"].sum()
+    for parameters in common_parameters:
-        return x
+        performance_sum = sum([r["relative_performance"] for r in common_results if r["parameters"] == parameters])
-    df = df.groupby(parameter_column_names).apply(sum_performance)
+        performance_sums.append(performance_sum)
 
-    # Retrieves the entries with the highest performance
+    # Retrieves the entry with the highest performance
-    best_performance = df["group_performance"].max()
+    best_index = argmax(performance_sums)
-    df_bests = df[df["group_performance"] == best_performance]
+    best_performance = performance_sums[best_index]
-
+    best_parameters = common_parameters[best_index]
-    # Retrieves one example only (the parameters are the same anyway)
-    df_bests = df_bests.drop_duplicates(["group_performance"])
 
     # Completed, report and return the results
     if verbose:
-        print("[database] " + str(name) + " with performance " + str(best_performance) + " " + str(df_bests.shape))
+        print("[database] " + str(name) + " with performance " + str(best_performance))
-    assert len(df_bests) == 1
+    return best_parameters
-    return df_bests

scripts/database/database/clblast.py

@@ -18,6 +18,7 @@ DEVICE_ATTRIBUTES = ["device", "device_core_clock", "device_compute_units"]
 KERNEL_ATTRIBUTES = ["precision", "kernel_family"]
 ARGUMENT_ATTRIBUTES = ["arg_m", "arg_n", "arg_k", "arg_alpha", "arg_beta"]
 ATTRIBUTES = DEVICE_ATTRIBUTES + DEVICE_TYPE_ATTRIBUTES + KERNEL_ATTRIBUTES + ARGUMENT_ATTRIBUTES
+GROUP_ATTRIBUTES = DEVICE_TYPE_ATTRIBUTES + KERNEL_ATTRIBUTES + ["kernel"] + ARGUMENT_ATTRIBUTES
 
 
 def precision_to_string(precision):
@@ -81,42 +82,63 @@ def print_cpp_database(database, output_dir):
     """Outputs the database as C++ code"""
 
     # Iterates over the kernel families
-    for family_name, family_database in database.groupby(["kernel_family"]):
+    kernel_families = sorted(set([s["kernel_family"] for s in database["sections"]]))
-        family_database = family_database.dropna(axis=1, how='all')
+    for family_name in kernel_families:
+        family_database = [s for s in database["sections"] if s["kernel_family"] == family_name]
 
         # Opens a new file for each kernel family
-        full_path = os.path.join(output_dir, family_name+'.hpp')
+        full_path = os.path.join(output_dir, family_name + ".hpp")
         with open(full_path, 'w+') as f:
             f.write(get_cpp_header(family_name))
 
             # Loops over the different precision (e.g. 16, 32, 3232, 64, 6464)
-            for precision, precision_database in family_database.groupby(["precision"]):
+            precisions = sorted(set([s["precision"] for s in database["sections"]]))  # Based on full database
+            for precision in precisions:
+                precision_database = [s for s in family_database if s["precision"] == precision]
                 f.write(get_cpp_precision(family_name, precision))
 
-                # Loops over a combination of device vendors and device types (e.g. AMD GPU)
+                # In case there is nothing found at all (e.g. 16-bit): continue as if this was a precision of 32 but
-                for vendor, vendor_database in precision_database.groupby(["device_vendor"]):
+                # with the defaults only
-                    for device_type, device_type_database in vendor_database.groupby(["device_type"]):
+                if len(precision_database) == 0:
+                    print("[database] No results found for %s:%s, retrieving defaults from %s:32" %
+                          (family_name, precision, family_name))
+                    precision_database = [s for s in family_database if s["precision"] == "32"
+                                          and s["device_vendor"] == VENDOR_DEFAULT
+                                          and s["device_type"] == DEVICE_TYPE_DEFAULT
+                                          and s["device"] == DEVICE_NAME_DEFAULT]
+
+                # Loops over device vendors (e.g. AMD)
+                device_vendors = sorted(set([s["device_vendor"] for s in precision_database]))
+                for vendor in device_vendors:
+                    vendor_database = [s for s in precision_database if s["device_vendor"] == vendor]
+
+                    # Loops over device types (e.g. GPU)
+                    device_types = sorted(set([s["device_type"] for s in vendor_database]))
+                    for device_type in device_types:
+                        type_database = [s for s in vendor_database if s["device_type"] == device_type]
                         f.write(get_cpp_device_vendor(vendor, device_type))
 
                         # Loops over every device of this vendor-type combination
-                        for device_name, device_database in device_type_database.groupby(["device"]):
+                        devices = sorted(set([s["device"] for s in type_database]))
+                        for device_name in devices:
+                            device_database = [s for s in type_database if s["device"] == device_name]
                             device_name_quoted = "\"%s\"," % device_name
                             device_name_cpp = "        { %-50s { " % device_name_quoted
                             f.write(device_name_cpp)
 
                             # Collects the parameters for this entry
                             parameters = []
-                            for kernel, kernel_database in device_database.groupby(["kernel"]):
+                            kernels = sorted(set([s["kernel"] for s in device_database]))
-                                kernel_database = kernel_database.dropna(axis=1)
+                            for kernel in kernels:
+                                kernel_database = [s for s in device_database if s["kernel"] == kernel]
 
-                                # Only consider the actual parameters, not the precision
+                                assert len(kernel_database) == 1
-                                def is_parameter(column):
+                                results = kernel_database[0]["results"]
-                                    return column.startswith('parameters.') and column != "parameters.PRECISION"
-                                column_names = [col for col in list(kernel_database) if is_parameter(col)]
 
-                                for p in column_names:
+                                assert len(results) == 1
-                                    parameter_name = p.replace("parameters.", "")
+                                new_parameters = results[0]["parameters"]
-                                    parameter_value = int(kernel_database[p].iloc[0])
+                                for parameter_name in sorted(new_parameters):
+                                    parameter_value = new_parameters[parameter_name]
                                     parameters.append("{\"" + parameter_name + "\"," + str(parameter_value) + "}")
 
                             # Prints the entry

scripts/database/database/db.py

@@ -5,66 +5,60 @@
 # Author(s):
 #   Cedric Nugteren <www.cedricnugteren.nl>
 
-import pandas as pd
+import clblast
-import numpy as np
 
 
-def get_entries_by_field(database, field, value):
+def length(database):
-    """Retrieves entries from the database with a specific value for a given field"""
+    """Computes the total number of tuning entries"""
-    return database[database[field] == value]
+    num_tuning_entries = 0
+    for section in database["sections"]:
+        num_tuning_entries += len(section["results"])
+    return num_tuning_entries
 
 
-def concatenate_database(database1, database2):
+def add_section(database, new_section):
-    """Concatenates two databases row-wise and returns the result"""
+    """Adds a new section to the database"""
-    return pd.concat([database1, database2])
+    for old_section in database["sections"]:
 
+        # Verify whether the sections match
+        equal = True
+        for attribute in new_section.keys():
+            if attribute != "results":
+                if attribute not in old_section or new_section[attribute] != old_section[attribute]:
+                    equal = False
+                    break
 
-def find_and_replace(database, dictionary):
+        # They match: append the new section's results to the corresponding entry in the database and return
-    """Finds and replaces entries in a database based on a dictionary. Example:
+        if equal:
-    dictionary = { "key_to_edit": { find1: replace1, find2, replace2 } }"""
+            old_section["results"] = combine_results(old_section["results"], new_section["results"])
-    return database.replace(dictionary)
+            return database
 
-
+    # No match found: append the whole new section to the database
-def remove_entries_by_key_value(database, key, value):
+    database["sections"].append(new_section)
-    """Removes entries in the databased which have a specific value for a given key"""
-    return database[database[key] != value]
-
-
-def remove_entries_by_device(database, device_name):
-    """Shorthand for the above, specifically removes entries for a given device"""
-    return remove_entries_by_key_value(database, "device", device_name)
-
-
-def remove_entries_by_kernel_family(database, kernel_family_name):
-    """Shorthand for the above, specifically removes entries for a given kernel family"""
-    return remove_entries_by_key_value(database, "kernel_family", kernel_family_name)
-
-
-def update_database(database, condition, field, value):
-    """Updates the database by writing a specific value to a given field, given certain conditions"""
-    database.loc[condition, field] = value
     return database
 
 
-def remove_duplicates(database):
+def combine_results(old_results, new_results):
-    """Removes duplicates from the database based on all but the 'time' column"""
+    """Adds new results to the results JSON list"""
+    for new_result in new_results:
+        old_results = combine_result(old_results, new_result)
+    return old_results
 
-    # First remove 100% duplicate entries
-    database = database.drop_duplicates()
 
-    # Replace NaNs with -1 first (needed for groupby)
+def combine_result(old_results, new_result):
-    database = database.replace(np.nan, -1)
+    """Adds a new result to the results JSON list; filters for duplicate entries and saves the best performing one"""
 
-    # In case multiple runs for the exact same configuration where made: take just the best performing one into account
+    # Loops over all existing results to test for already existing entries with these parameters
-    other_column_names = list(database.columns.values)
+    for old_result in old_results:
-    other_column_names.remove("time")
-    database_by_time = database.groupby(other_column_names,)
-    num_removals = len(database) - len(database_by_time)
-    if num_removals > 0:
-        print("[database] Removing %d entries: keeping only those with the lowest execution time" % num_removals)
-        print("[database] Note: this might take a while")
-        database = database_by_time.apply(lambda x: x[x["time"] == x["time"].min()])
 
-    # Re-replace the NaN values
+        # Verify whether the results match
-    database = database.replace(-1, np.nan)
+        equal = new_result["parameters"] == old_result["parameters"]
-    return database
+
+        # They match: keep only the one with the minimum execution time
+        if equal:
+            old_result["time"] = min(old_result["time"], new_result["time"])
+            return old_results
+
+    # No match found: append a new result
+    old_results.append(new_result)
+    return old_results

scripts/database/database/defaults.py

@@ -5,102 +5,176 @@
 # Author(s):
 #   Cedric Nugteren <www.cedricnugteren.nl>
 
-import pandas as pd
 
 import clblast
 import bests
 
 
-def set_default_device(database_entry):
+def set_default_device(section):
     """Sets the device name and parameters to some default values"""
-    database_entry["device"] = clblast.DEVICE_NAME_DEFAULT
+    section["device"] = clblast.DEVICE_NAME_DEFAULT
-    database_entry["device_compute_units"] = 0
+    section["device_compute_units"] = 0
-    database_entry["device_core_clock"] = 0
+    section["device_core_clock"] = 0
-    return database_entry
+    return section
 
 
-def set_default_time(database_entry):
+def set_identifiers(database, group_by_attributes, identifier_name):
-    """Sets the execution time to some default value"""
+    """Sets a group-identifier based on a given set of attributes. Modifies the database but also returns a list of
-    database_entry["time"] = 0.0
+    unique identifiers."""
-    return database_entry
+    identifiers = []
+    for section in database["sections"]:
+        identifier = []
+        for attribute in group_by_attributes:
+            if attribute in section:
+                identifier.append(section[attribute])
+        section[identifier_name] = ";".join(identifier)
+        identifiers.append(section[identifier_name])
+    return sorted(set(identifiers))
 
 
-def calculate_defaults(database, verbose, calculate_common_best=True):
+def remove_identifiers(database, identifier_name):
-    """Sets defaults for devices of the same type/vendor. An option determines how to compute the defaults."""
+    """Removes an identifier from all sections in the database"""
-    database_defaults = pd.DataFrame()
+    for section in database["sections"]:
+        section.pop(identifier_name, None)
 
-    # Defaults per combination of device vendors and device types (e.g. AMD GPU)
-    database_type_vendor = database.groupby(clblast.DEVICE_TYPE_ATTRIBUTES + clblast.KERNEL_ATTRIBUTES + ["kernel"] +
-                                            clblast.ARGUMENT_ATTRIBUTES)
-    for group_name, database_group in database_type_vendor:
-        if calculate_common_best:
-            default_values = get_common_best(database_group, group_name, verbose)
-        else:
-            default_values = get_smallest_best(database_group)
-        default_values = set_default_device(default_values)
-        default_values = set_default_time(default_values)
-        database_defaults = database_defaults.append(default_values, ignore_index=True)
 
-    # Checks for mis-matched arguments
+def get_groups_by_identifier(database, group_identifiers, identifier_name):
-    groups = database_defaults.groupby(clblast.DEVICE_TYPE_ATTRIBUTES + clblast.KERNEL_ATTRIBUTES + ["kernel"])
+    """Returns a list of (group, group_identifier) tuples based a previously made grouping"""
-    for group_name, database_group in groups:
+    groups = []
-        if len(database_group) != 1:
+    for group_identifier in group_identifiers:
-            print("[WARNING] Entries for a single kernel with multiple argument values: " + str(group_name))
 
-    # Defaults over all device types and vendors
+        # Get all sections in this group
-    groups = database.groupby(clblast.KERNEL_ATTRIBUTES + ["kernel"] + clblast.ARGUMENT_ATTRIBUTES)
+        group = []
-    for group_name, database_group in groups:
+        for section in database["sections"]:
-        if calculate_common_best:
+            if section[identifier_name] == group_identifier:
-            default_values = get_common_best(database_group, group_name, verbose)
+                group.append(section)
-        else:
+
-            default_values = get_smallest_best(database_group)
+        groups.append((group, group_identifier))
-        default_values["device_vendor"] = clblast.VENDOR_DEFAULT
+    return groups
-        default_values["device_type"] = clblast.DEVICE_TYPE_DEFAULT
+
-        default_values = set_default_device(default_values)
+
-        default_values = set_default_time(default_values)
+def calculate_defaults(database, verbose):
-        database_defaults = database_defaults.append(default_values, ignore_index=True)
+    """Sets defaults for devices of the same type/vendor"""
+
+    # Groups the database by kernel, vendor and device type (e.g. AMD GPU)
+    group_identifiers = set_identifiers(database, clblast.GROUP_ATTRIBUTES, "group_identifier")
+    groups = get_groups_by_identifier(database, group_identifiers, "group_identifier")
+
+    # Loops over all groups
+    default_sections = {"sections": []}
+    for group, group_identifier in groups:
+
+        # Computes the best parameters
+        default_parameters = get_common_best_parameters(group, group_identifier, verbose)
+
+        # Stores all the section's data
+        assert len(group) > 0
+        default_section = {}
+        for attribute in group[0].keys():
+            if attribute != "results" and attribute != "group_identifier":
+                default_section[attribute] = group[0][attribute]
+        default_section = set_default_device(default_section)
+        default_section["results"] = [{"time": 0.0, "parameters": default_parameters}]
+        default_sections["sections"].append(default_section)
+
+    # Groups the database by kernel, vendor and device type (e.g. AMD GPU) - but not by arguments! This is to check for
+    # mis-matched arguments.
+    attributes = clblast.DEVICE_TYPE_ATTRIBUTES + clblast.KERNEL_ATTRIBUTES + ["kernel"]
+    group_identifiers = set_identifiers(default_sections, attributes, "temp_identifier")
+    groups = get_groups_by_identifier(default_sections, group_identifiers, "temp_identifier")
+    for group, group_identifier in groups:
+        if len(group) != 1:
+            print("[ERROR] Entries for a single kernel with multiple argument values: " + str(group_identifier))
+        assert len(group) == 1
+    remove_identifiers(default_sections, "temp_identifier")
+
+    # Groups the database by kernel only
+    group_identifiers = set_identifiers(database, clblast.KERNEL_ATTRIBUTES + ["kernel"], "group_identifier")
+    groups = get_groups_by_identifier(database, group_identifiers, "group_identifier")
+
+    # Loops over all groups
+    for group, group_identifier in groups:
+
+        # Computes the best parameters
+        default_parameters = get_common_best_parameters(group, group_identifier, verbose)
+
+        # Stores all the section's data
+        assert len(group) > 0
+        default_section = {}
+        for attribute in group[0].keys():
+            if attribute != "results" and attribute != "group_identifier":
+                default_section[attribute] = group[0][attribute]
+        default_section = set_default_device(default_section)
+        default_section["device_vendor"] = clblast.VENDOR_DEFAULT
+        default_section["device_type"] = clblast.DEVICE_TYPE_DEFAULT
+        default_section["results"] = [{"time": 0.0, "parameters": default_parameters}]
+        default_sections["sections"].append(default_section)
 
     # Database with both types of defaults only
-    return database_defaults
+    return default_sections
 
 
-def get_smallest_best(database):
+def get_smallest_best_parameters(group):
     """Sets defaults based on the smallest values of all known entries. The average might be better for performance but
     some parameters might not be supported on other devices."""
-    database_best_results = bests.get_best_results(database)
+
-    return database_best_results.min(axis=0)
+    # Counts the number of devices in this group
+    assert len(group) > 0
+
+    # Find the smallest values of the parameters
+    min_parameters = {}
+    for section in group:
+        assert len(section["results"]) > 0
+        minimum_time = min([result["time"] for result in section["results"]])
+        for result in section["results"]:
+            if result["time"] == minimum_time:
+                for parameter in result["parameters"]:
+                    if parameter in min_parameters:
+                        min_parameters[parameter] = min(min_parameters[parameter], result["parameters"][parameter])
+                    else:
+                        min_parameters[parameter] = result["parameters"][parameter]
+
+    return min_parameters
 
 
-def get_common_best(database, group_name, verbose):
+def get_common_best_parameters(group, group_identifier, verbose):
     """Sets defaults based on the best values of entries supported by all devices. This might cause a problem in case
     not every device was tuned with the same parameters. In that case it falls back to the above method to retrieve
     the smallest best execution time"""
 
     # Counts the number of devices in this group
-    num_devices = len(database.groupby(clblast.DEVICE_ATTRIBUTES))
+    num_devices = len(group)
-
+    assert num_devices > 0
-    # Removes columns without any values
-    database = database.dropna(axis=1, how='all')
 
     # Inserts the relative execution times into the database
-    def relative_performance(x):
+    for section in group:
-        x["relative_performance"] = x["time"].min() / x["time"]
+        assert len(section["results"]) > 0
-        return x
+        minimum_time = min([result["time"] for result in section["results"]])
-    database = database.groupby(clblast.ATTRIBUTES + ["kernel"]).apply(relative_performance)
+        for result in section["results"]:
+            result["relative_performance"] = minimum_time / result["time"]
 
-    # Retrieves the parameter names for this kernel
+    # Determine which parameters are available for all devices
-    all_column_names = list(database.columns.values)
+    common_parameters = [result["parameters"] for result in group[0]["results"]]  # Parameters of the first section
-    parameter_column_names = [c for c in all_column_names if "parameters." in c]
+    for i in range(1, num_devices):
-
+        section_parameters = [result["parameters"] for result in group[i]["results"]]
-    # Removes entries which are not available for all devices
+        common_parameters = [p for p in section_parameters if p in common_parameters]  # Intersection of the parameters
-    database_by_parameters = database.groupby(parameter_column_names)
-    database_common = database_by_parameters.filter(lambda x: len(x) == num_devices)
 
     # Fall back to another method in case there are no shared entries at all across devices
-    if len(database_common) == 0:
+    if len(common_parameters) == 0:
         if verbose:
-            print("[database] No common kernels for: " + str(group_name) + " with devices: %d " % num_devices)
+            print("[database] No common kernels for: " + str(group_identifier) + " with devices: %d " % num_devices)
-        return get_smallest_best(database)
+        smallest_best_parameters = get_smallest_best_parameters(group)
+        if verbose:
+            print("[database] " + str(group_identifier))
+        return smallest_best_parameters
+
+    # Removes entries with parameters which are not common
+    common_results = []
+    for section in group:
+        for result in section["results"]:
+            if result["parameters"] in common_parameters:
+                common_results.append(result)
 
     # Retrieves the entries with the highest relative performance
-    return bests.get_relative_bests(database_common, parameter_column_names, group_name, verbose)
+    relative_best_parameters = bests.get_relative_bests(group_identifier, common_results, common_parameters, verbose)
+    return relative_best_parameters

scripts/database/database/io.py

@@ -13,46 +13,48 @@ try:
 except ImportError:
     from urllib2 import urlopen  # Python 2
 
-import pandas as pd
-
-import clblast
-
 
 def download_database(filename, database_url):
     """Downloads a database and saves it to disk"""
     print("[database] Downloading database from '" + database_url + "'...")
     database = urlopen(database_url)
-    with open(filename, 'wb') as f:
+    with open(filename, "wb") as f:
         f.write(database.read())
 
 
 def load_database(filename):
     """Loads a database from disk"""
     print("[database] Loading database from '" + filename + "'")
-    return pd.read_pickle(filename)
+    with open(filename) as f:
+        return json.load(f)
 
 
 def save_database(database, filename):
     """Saves a database to disk"""
     print("[database] Saving database to '" + filename + "'")
-    database.to_pickle(filename)
+    with open(filename, "wb") as f:
+        json.dump(database, f, sort_keys=True, indent=4)
 
 
-def load_json_to_pandas(filename):
+def load_tuning_results(filename):
-    """Loads JSON data from file and converts it to a pandas database"""
+    """Loads JSON data from file and pre-processes it"""
     with open(filename) as f:
         json_data = json.load(f)
 
-    # Gathers all results and stores them in a new database
+    # Removes the numbering following the kernel family name
-    json_database = pd.DataFrame(json_data)
+    json_data["kernel_family"] = re.sub(r'_\d+', '', json_data["kernel_family"])
-    new_database = pd.io.json.json_normalize(json_database["results"])
 
-    # Sets the common attributes to each entry in the results
+    # Adds the kernel name to the section instead of to the individual results
-    for attribute in clblast.ATTRIBUTES:
+    assert len(json_data["results"]) > 0
-        if attribute == "kernel_family":
+    json_data["kernel"] = json_data["results"][0]["kernel"]
-            new_database[attribute] = re.sub(r'_\d+', '', json_data[attribute])
+    for result in json_data["results"]:
-        elif attribute in json_data:
+        assert json_data["kernel"] == result["kernel"]
-            new_database[attribute] = json_data[attribute]
+        result.pop("kernel", None)
-        else:
+
-            new_database[attribute] = 0  # For example a parameters that was not used by this kernel
+    # Removes the 'PRECISION' parameter from the individual results: it is redundant
-    return new_database
+    for result in json_data["results"]:
+        assert json_data["precision"] == str(result["parameters"]["PRECISION"])
+        result["parameters"].pop("PRECISION", None)
+
+    # All done
+    return json_data

src/database/database.cpp

@@ -35,9 +35,9 @@ const std::vector<Database::DatabaseEntry> Database::database = {
   XdotHalf, XdotSingle, XdotDouble, XdotComplexSingle, XdotComplexDouble,
   XgemvHalf, XgemvSingle, XgemvDouble, XgemvComplexSingle, XgemvComplexDouble,
   XgemvFastHalf, XgemvFastSingle, XgemvFastDouble, XgemvFastComplexSingle, XgemvFastComplexDouble,
-  /* XgemvFastRotHalf, */ XgemvFastRotSingle, XgemvFastRotDouble, XgemvFastRotComplexSingle, XgemvFastRotComplexDouble,
+  XgemvFastRotHalf, XgemvFastRotSingle, XgemvFastRotDouble, XgemvFastRotComplexSingle, XgemvFastRotComplexDouble,
   XgerHalf, XgerSingle, XgerDouble, XgerComplexSingle, XgerComplexDouble,
-  /* XgemmHalf, */ XgemmSingle, XgemmDouble, XgemmComplexSingle, XgemmComplexDouble,
+  XgemmHalf, XgemmSingle, XgemmDouble, XgemmComplexSingle, XgemmComplexDouble,
   CopyHalf, CopySingle, CopyDouble, CopyComplexSingle, CopyComplexDouble,
   PadHalf, PadSingle, PadDouble, PadComplexSingle, PadComplexDouble,
   TransposeHalf, TransposeSingle, TransposeDouble, TransposeComplexSingle, TransposeComplexDouble,

src/database/database.hpp

@@ -72,9 +72,9 @@ class Database {
   static const DatabaseEntry XdotHalf, XdotSingle, XdotDouble, XdotComplexSingle, XdotComplexDouble;
   static const DatabaseEntry XgemvHalf, XgemvSingle, XgemvDouble, XgemvComplexSingle, XgemvComplexDouble;
   static const DatabaseEntry XgemvFastHalf, XgemvFastSingle, XgemvFastDouble, XgemvFastComplexSingle, XgemvFastComplexDouble;
-  static const DatabaseEntry /* XgemvFastRotHalf, */ XgemvFastRotSingle, XgemvFastRotDouble, XgemvFastRotComplexSingle, XgemvFastRotComplexDouble;
+  static const DatabaseEntry XgemvFastRotHalf, XgemvFastRotSingle, XgemvFastRotDouble, XgemvFastRotComplexSingle, XgemvFastRotComplexDouble;
   static const DatabaseEntry XgerHalf, XgerSingle, XgerDouble, XgerComplexSingle, XgerComplexDouble;
-  static const DatabaseEntry /* XgemmHalf, */ XgemmSingle, XgemmDouble, XgemmComplexSingle, XgemmComplexDouble;
+  static const DatabaseEntry XgemmHalf, XgemmSingle, XgemmDouble, XgemmComplexSingle, XgemmComplexDouble;
   static const DatabaseEntry CopyHalf, CopySingle, CopyDouble, CopyComplexSingle, CopyComplexDouble;
   static const DatabaseEntry PadHalf, PadSingle, PadDouble, PadComplexSingle, PadComplexDouble;
   static const DatabaseEntry TransposeHalf, TransposeSingle, TransposeDouble, TransposeComplexSingle, TransposeComplexDouble;

src/database/kernels/xaxpy.hpp

@@ -20,12 +20,12 @@ const Database::DatabaseEntry Database::XaxpyHalf = {
       kDeviceTypeGPU, "Intel", {
         { "Intel(R) HD Graphics 5500 BroadWell U-Processor GT2", { {"VW",1}, {"WGS",64}, {"WPT",1} } },
         { "Intel(R) HD Graphics Skylake ULT GT2",            { {"VW",4}, {"WGS",512}, {"WPT",8} } },
-        { "default",                                         { {"VW",4}, {"WGS",512}, {"WPT",8} } },
+        { "default",                                         { {"VW",8}, {"WGS",64}, {"WPT",1} } },
       }
     },
     { // Default
       kDeviceTypeAll, "default", {
-        { "default",                                         { {"VW",4}, {"WGS",512}, {"WPT",8} } },
+        { "default",                                         { {"VW",8}, {"WGS",64}, {"WPT",1} } },
       }
     },
   }

src/database/kernels/xdot.hpp

@@ -38,11 +38,10 @@ const Database::DatabaseEntry Database::XdotSingle = {
     { // AMD GPUs
       kDeviceTypeGPU, "AMD", {
         { "AMD Radeon R9 M370X Compute Engine",              { {"WGS1",128}, {"WGS2",32} } },
-        { "Hawaii",                                          { {"WGS1",256}, {"WGS2",32} } },
         { "Oland",                                           { {"WGS1",256}, {"WGS2",32} } },
         { "Pitcairn",                                        { {"WGS1",128}, {"WGS2",32} } },
         { "Tahiti",                                          { {"WGS1",128}, {"WGS2",32} } },
-        { "default",                                         { {"WGS1",256}, {"WGS2",32} } },
+        { "default",                                         { {"WGS1",128}, {"WGS2",32} } },
       }
     },
     { // Intel CPUs
@@ -90,11 +89,10 @@ const Database::DatabaseEntry Database::XdotComplexSingle = {
     { // AMD GPUs
       kDeviceTypeGPU, "AMD", {
         { "AMD Radeon R9 M370X Compute Engine",              { {"WGS1",64}, {"WGS2",32} } },
-        { "Hawaii",                                          { {"WGS1",256}, {"WGS2",32} } },
         { "Oland",                                           { {"WGS1",128}, {"WGS2",32} } },
         { "Pitcairn",                                        { {"WGS1",256}, {"WGS2",32} } },
         { "Tahiti",                                          { {"WGS1",64}, {"WGS2",32} } },
-        { "default",                                         { {"WGS1",256}, {"WGS2",32} } },
+        { "default",                                         { {"WGS1",128}, {"WGS2",32} } },
       }
     },
     { // Intel CPUs
@@ -142,7 +140,6 @@ const Database::DatabaseEntry Database::XdotDouble = {
     { // AMD GPUs
       kDeviceTypeGPU, "AMD", {
         { "AMD Radeon R9 M370X Compute Engine",              { {"WGS1",64}, {"WGS2",128} } },
-        { "Hawaii",                                          { {"WGS1",256}, {"WGS2",32} } },
         { "Oland",                                           { {"WGS1",256}, {"WGS2",32} } },
         { "Pitcairn",                                        { {"WGS1",128}, {"WGS2",32} } },
         { "Tahiti",                                          { {"WGS1",256}, {"WGS2",32} } },
@@ -184,7 +181,6 @@ const Database::DatabaseEntry Database::XdotComplexDouble = {
     { // AMD GPUs
       kDeviceTypeGPU, "AMD", {
         { "AMD Radeon R9 M370X Compute Engine",              { {"WGS1",64}, {"WGS2",32} } },
-        { "Hawaii",                                          { {"WGS1",256}, {"WGS2",32} } },
         { "Oland",                                           { {"WGS1",256}, {"WGS2",32} } },
         { "Pitcairn",                                        { {"WGS1",256}, {"WGS2",32} } },
         { "Tahiti",                                          { {"WGS1",256}, {"WGS2",32} } },

src/database/kernels/xgemm.hpp

@@ -14,6 +14,18 @@
 namespace clblast {
 // =================================================================================================
 
+const Database::DatabaseEntry Database::XgemmHalf = {
+  "Xgemm", Precision::kHalf, {
+    { // Default
+      kDeviceTypeAll, "default", {
+        { "default",                                         { {"KWG",16}, {"KWI",2}, {"MDIMA",8}, {"MDIMC",8}, {"MWG",32}, {"NDIMB",8}, {"NDIMC",8}, {"NWG",16}, {"SA",0}, {"SB",0}, {"STRM",0}, {"STRN",0}, {"VWM",1}, {"VWN",1} } },
+      }
+    },
+  }
+};
+
+// =================================================================================================
+
 const Database::DatabaseEntry Database::XgemmSingle = {
   "Xgemm", Precision::kSingle, {
     { // AMD GPUs
@@ -76,7 +88,7 @@ const Database::DatabaseEntry Database::XgemmSingle = {
     },
     { // Default
       kDeviceTypeAll, "default", {
-        { "default",                                         { {"KWG",16}, {"KWI",2}, {"MDIMA",8}, {"MDIMC",8}, {"MWG",32}, {"NDIMB",8}, {"NDIMC",8}, {"NWG",64}, {"SA",0}, {"SB",0}, {"STRM",0}, {"STRN",0}, {"VWM",1}, {"VWN",1} } },
+        { "default",                                         { {"KWG",16}, {"KWI",2}, {"MDIMA",8}, {"MDIMC",8}, {"MWG",32}, {"NDIMB",8}, {"NDIMC",8}, {"NWG",16}, {"SA",0}, {"SB",0}, {"STRM",0}, {"STRN",0}, {"VWM",1}, {"VWN",1} } },
       }
     },
   }

src/database/kernels/xgemv.hpp

@@ -20,12 +20,12 @@ const Database::DatabaseEntry Database::XgemvHalf = {
       kDeviceTypeGPU, "Intel", {
         { "Intel(R) HD Graphics 5500 BroadWell U-Processor GT2", { {"WGS1",64}, {"WPT1",1} } },
         { "Intel(R) HD Graphics Skylake ULT GT2",            { {"WGS1",128}, {"WPT1",1} } },
-        { "default",                                         { {"WGS1",128}, {"WPT1",1} } },
+        { "default",                                         { {"WGS1",64}, {"WPT1",1} } },
       }
     },
     { // Default
       kDeviceTypeAll, "default", {
-        { "default",                                         { {"WGS1",128}, {"WPT1",1} } },
+        { "default",                                         { {"WGS1",64}, {"WPT1",1} } },
       }
     },
   }

src/database/kernels/xgemv_fast.hpp

@@ -20,12 +20,12 @@ const Database::DatabaseEntry Database::XgemvFastHalf = {
       kDeviceTypeGPU, "Intel", {
         { "Intel(R) HD Graphics 5500 BroadWell U-Processor GT2", { {"VW2",1}, {"WGS2",16}, {"WPT2",1} } },
         { "Intel(R) HD Graphics Skylake ULT GT2",            { {"VW2",2}, {"WGS2",128}, {"WPT2",2} } },
-        { "default",                                         { {"VW2",2}, {"WGS2",128}, {"WPT2",2} } },
+        { "default",                                         { {"VW2",1}, {"WGS2",16}, {"WPT2",1} } },
       }
     },
     { // Default
       kDeviceTypeAll, "default", {
-        { "default",                                         { {"VW2",2}, {"WGS2",128}, {"WPT2",2} } },
+        { "default",                                         { {"VW2",1}, {"WGS2",16}, {"WPT2",1} } },
       }
     },
   }

src/database/kernels/xgemv_fast_rot.hpp

@@ -14,6 +14,18 @@
 namespace clblast {
 // =================================================================================================
 
+const Database::DatabaseEntry Database::XgemvFastRotHalf = {
+  "XgemvFastRot", Precision::kHalf, {
+    { // Default
+      kDeviceTypeAll, "default", {
+        { "default",                                         { {"VW3",8}, {"WGS3",32}, {"WPT3",32} } },
+      }
+    },
+  }
+};
+
+// =================================================================================================
+
 const Database::DatabaseEntry Database::XgemvFastRotSingle = {
   "XgemvFastRot", Precision::kSingle, {
     { // AMD GPUs