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Category: Development

OpenMP 5.0

OpenMP 5.0

Jan22019

The new OpenMP 5.0 API released

OpenMP is probably the most used API to parallelize applications running on multicore processors and with the last update it is also GPU accelerators.

OpenMP is an application program interface that supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran,on most platforms, instruction set architectures and operating systems, including Solaris, AIX, HP-UX, Linux, macOS, and Windows. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior.

The latest OpenMP 5.0 API are released in November 2018 and they are probably the most important update to the framework since the inaugural release in 1997.
Some of the new features of the new API were under development for more than five years.

With OpenMP 5.0, the biggest change with regard to GPUs is support for unified shared memory between the GPU accelerators and the CPU hosts in a hybrid system. “You don’t have to transfer ownership of data any more, you can just access the CPU and GPU regions,” says Michael Klemm, who is chief executive officer for the OpenMP Architecture Review Board Consortium.

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Apache Hivemall

Apache Hivemall

Dec292018

Big Data and Apache Hivemall: Machine Learning with SQL

Anyone use Machine Learning techniques applied to data know very well that this is a complex task that require knowledge of many programming languages, science, framework, algorithms and it means not a short work.

With Hivemall, Apache provides a scalable library of machine learning that is built as a collection of Hive User Defined Functions that allow anyone to run algorithms of machine learning with knowledge of SQL. Hivemall runs on Hadoop-based data processing frameworks, a distributed file system, with MapReduce parallel data processing model in this ecosystem. It is possible to try these functionalities in Apache Hive or Spark environments.

Apache Hivemall, such as Google BigQuery ML, allows us to apply machine learning to our big data with a series of queries. These solutions introduce a new paradigm “machine learning in query language” and basically these have the same logical approach but Hivemall is more flexible in terms of selection of algorithms and platforms.

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