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Intel DAAL Accelerates Data Analytics and Machine Learning

Intel DAAL is a high-performance library specifically optimized for big data analysis on the latest Intel platforms, including Intel Xeon®, and Intel Xeon Phi™. It provides the algorithmic building blocks for all stages in data analysis in offline, batch, streaming, and distributed processing environments. It was designed for efficient use over all the popular data platforms and APIs in use today, including MPI, Hadoop, Spark, R, MATLAB, Python, C++, and Java.

Six Steps Towards Better Performance on Intel Xeon Phi

“As with all new technology, developers will have to create processes in order to modernize applications to take advantage of any new feature. Rather than randomly trying to improve the performance of an application, it is wise to be very familiar with the application and use available tools to understand bottlenecks and look for areas of improvement.”

Intel Releases Optimized Python for HPC

“By implementing popular Python packages such as NumPy, SciPy, scikit-learn, to call the Intel Math Kernel Library (Intel MKL) and the Intel Data Analytics Acceleration Library (Intel DAAL), Python applications are automatically optimized to take advantage of the latest architectures. These libraries have also been optimized for multithreading through calls to the Intel Threading Building Blocks (Intel TBB) library. This means that existing Python applications will perform significantly better merely by switching to the Intel distribution.”

Vectorization Leads to Performance Gains

Applications that can take advantage of the new vectorization capabilities of the Intel Xeon Phi processor will show tremendous performance gains. “When considering vectorization, there are different tools that can assist the developer in determining where to look further. The first is to look at the optimization reports that are generated by the Intel compiler and then to also use the Vector Analyzer that can give specific advice on what to do to get more vectorization from the code.”

Achieving High-Performance Math Processing with Intel MKL 2017

“Many of the libraries developed in the 70s and 80s for core linear algebra and scientific math computation, such as BLAS, LAPACK, FFT, are still in use today with C, C++, Fortran, and even Python programs. With MKL, Intel has engineered a ready-to-use, royalty-free library that implements these numerical algorithms optimized specifically to take advantage of the latest features of Intel chip architectures. Even the best compiler can’t compete with the level of performance possible from a hand-optimized library. Any application that already relies on the BLAS or LAPACK functionality will achieve better performance on Intel and compatible architectures just by downloading and re-linking with Intel MKL.”

Managing Lots of Tasks for Intel Xeon Phi

“OpenMP, Fortran 2008 and TBB are standards that can help to create parallel areas of an application. MKL could also be considered to be part of this family, because it uses OpenMP within the library. OpenMP is well known and has been used for quite some time and is continues to be enhanced. Some estimates are as high as 75 % of cycles used are for Fortran applications. Thus, in order to modernize some of the most significant number crunchers today, Fortran 2008 should be investigated. TBB is for C++ applications only, and does not require compiler modifications. An additional benefit to using OpenMP and Fortran 2008 is that these are standards, which allows code to be more portable.”

A Decade of Multicore Parallelism with Intel TBB

While HPC developers worry about squeezing out the ultimate performance while running an application on dedicated cores, Intel TBB tackles a problem that HPC users never worry about: How can you make parallelism work well when you share the cores that you run upon?” This is more of a concern if you’re running that application on a many-core laptop or workstation than a dedicated supercomputer because who knows what will also be running on those shared cores. Intel Threaded Building Blocks reduce the delays from other applications by utilizing a revolutionary task-stealing scheduler. This is the real magic of TBB.

Programming for High Performance Processors

“Managing the work on each node can be referred to as Domain parallelism. During the run of the application, the work assigned to each node can be generally isolated from other nodes. The node can work on its own and needs little communication with other nodes to perform the work. The tools that are needed for this are MPI for the developer, but can take advantage of frameworks such as Hadoop and Spark (for big data analytics). Managing the work for each core or thread will need one level down of control. This type of work will typically invoke a large number of independent tasks that must then share data between the tasks.”

Speed Your Application with Threading Building Blocks

With modern processors that contain a large number of cores, to get maximum performance it is necessary to structure an application to use as many cores as possible. Explicitly developing a program to do this can take a significant amount of effort. It is important to understand the science and algorithms behind the application, and then use whatever programming techniques that are available. “Intel Threaded Building Blocks (TBB) can help tremendously in the effort to achieve very high performance for the application.”

Fast Networking for Next Generation Systems

“The Intel Omni-Path Architecture is an example of a networking system that has been designed for the Exascale era. There are many features that will enable this massive scaling of compute resources. Features and functionality are designed in at both the host and the fabric levels. This enables very large scaling when all of the components are designed together. Increased reliability is a result of integrating the CPU and fabric, which will be critical as the number of nodes expands well beyond any system in operation today. In addition, tools and software that have been designed to be installed and managed at the very large number of compute nodes that will be necessary to achieve this next level of performance.”