數據源加速見官方文檔（必須使用DAAL自己的庫）：

Data Management

• Numeric Tables
• Tensors
• Data Sources
• Data Dictionaries
• Data Serialization andDeserialization
• Data Compression
• Data Model

可以看到支持的數據源：同數據類型的table（matrix），不同類型的table，以及從DB文件取數據、數據序列化、壓縮等。

在這些定制的數據源上，Intel DAAL使用自己底層的CPU進行硬件加速！下面摘自其官方：

Intel DAAL addresses all stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling

, validation, and decision-making.

Intel DAAL is developed by the same team as the Intel? Math Kernel Library (Intel? MKL)—the leading math library in the world. This team works closely with Intel? processor architects to squeeze performance from Intel processor-based systems.

Specs at a Glance

# file: low_order_moms_dense_batch.py
#===============================================================================
# Copyright 2014-2018 Intel Corporation.
#
# This software and the related documents are Intel copyrighted  materials,  and
# your use of  them is  governed by the  express license  under which  they were
# provided to you (License).  Unless the License provides otherwise, you may not
# use, modify, copy, publish, distribute,  disclose or transmit this software or
# the related documents without Intel‘s prior written permission.
#
# This software and the related documents  are provided as  is,  with no express
# or implied  warranties,  other  than those  that are  expressly stated  in the
# License.
#===============================================================================

## <a name="DAAL-EXAMPLE-PY-LOW_ORDER_MOMENTS_DENSE_BATCH"></a>
## \example low_order_moms_dense_batch.py

import os
import sys

from daal.algorithms import low_order_moments
from daal.data_management import FileDataSource, DataSourceIface

utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
if utils_folder not in sys.path:
    sys.path.insert(0, utils_folder)
from utils import printNumericTable

DAAL_PREFIX = os.path.join(‘..‘, ‘data‘)

# Input data set parameters
dataFileName = os.path.join(DAAL_PREFIX, ‘batch‘, ‘covcormoments_dense.csv‘)


def printResults(res):
    printNumericTable(res.get(low_order_moments.minimum),              "Minimum:")
    printNumericTable(res.get(low_order_moments.maximum),              "Maximum:")
    printNumericTable(res.get(low_order_moments.sum),                  "Sum:")
    printNumericTable(res.get(low_order_moments.sumSquares),           "Sum of squares:")
    printNumericTable(res.get(low_order_moments.sumSquaresCentered),   "Sum of squared difference from the means:")
    printNumericTable(res.get(low_order_moments.mean),                 "Mean:")
    printNumericTable(res.get(low_order_moments.secondOrderRawMoment), "Second order raw moment:")
    printNumericTable(res.get(low_order_moments.variance),             "Variance:")
    printNumericTable(res.get(low_order_moments.standardDeviation),    "Standard deviation:")
    printNumericTable(res.get(low_order_moments.variation),            "Variation:")

if __name__ == "__main__":

    # Initialize FileDataSource to retrieve input data from .csv file
    dataSource = FileDataSource(
        dataFileName,
        DataSourceIface.doAllocateNumericTable,
        DataSourceIface.doDictionaryFromContext
    )

    # Retrieve the data from input file
    dataSource.loadDataBlock()

    # Create algorithm for computing low order moments in batch processing mode
    algorithm = low_order_moments.Batch()

    # Set input arguments of the algorithm
    algorithm.input.set(low_order_moments.data, dataSource.getNumericTable())

    # Get computed low order moments
    res = algorithm.compute()

    printResults(res)　　

Intel DAAL AI加速——支持從數據預處理到模型預測，數據源必須使用DAAL的底層封裝庫