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The news in this category has been selected by us because we thought it would be interestingto hard core cluster geeks. Of course, you don't have to be a cluster geek to read the news stories.

From the language-not-the-movie(s) department

Two important Julia Language updates. First, a great interview over at RCE-Cast (while you are there listen to the Singularity interview as well). Head over an take a listen.

What is Julia you ask? A good answer comes from the Julia team:

Julia is the open source programming language for data science and numerical computing that is taking many diverse areas such finance, central banking, insurance, engineering, robotics, artificial intelligence, astrophysics, life sciences and many others by storm. Julia combines the functionality of quantitative environments such as Python and R with the speed of production languages like C++, Fortran and Java to solve big data and analytics problems. Julia delivers dramatic improvements in simplicity, speed, capacity and productivity for data scientists, quants and researchers who need to solve massive computation problems quickly and accurately. The number of Julia users has grown dramatically during the last five years – doubling every 9 months. Julia is taught at MIT, Stanford and dozens of universities worldwide, including MOOCs on Coursera and EdX.

Update: Intel releases ParallelAccelerator v0.2 for Julia 0.5

From the bad-play-on-words department

For those using Python to calculate asymptotes and other science and mathematical things, Intel ® has added its speedy MKL (Math Kernel Library) to the mix. Called Intel ® Distribution for Python* 2017 Beta, The beta release gives Python a big boost by using MKL and other libraries. From the web page "The Beta product adds new Python packages like scikit-learn, mpi4py, numba, conda, tbb (Python interfaces to Intel Threading Building Blocks) and pyDAAL (Python interfaces to Intel Data Analytics Acceleration Library). The Beta also delivers performance improvements for NumPy/SciPy through linking with performance libraries like Intel MKL, Intel Message Passing Interface (Intel MPI), Intel TBB and Intel DAAL."

Beta users can look forward to the following features.

  • Includes NumPy, SciPy, scikit-learn, numba, Cython, pyDAAL
  • Performance accelerations via Intel® MKL, Intel MPI, Intel® TBB, Intel® DAAL
  • Easy, out-of-the-box access to performance
  • Free to download
  • Supports Python versions 2.7 and 3.5
  • Available on Windows*, Linux, and Mac OS

An Intel blog provide more information. There is also a Python profiling tool (beta) available.

A recent article on has announced a breakthrough in quantum computing. The article, Crucial hurdle overcome in quantum computing, describes how a team at University of New South Wales (UNSW) in Sydney Australia has created a working quantum gate in silicon. This process paves the way for quantum computing to become a reality in the years to come. Background on quantum computing can be found in this Cluster Monkey article: A Smidgen of Quantum Computing

According to Dr. Menno Veldhorst, a UNSW Research Fellow and the lead author of the Nature paper:

"We've morphed those silicon transistors into quantum bits by ensuring that each has only one electron associated with it. We then store the binary code of 0 or 1 on the 'spin' of the electron, which is associated with the electron's tiny magnetic field."

From the best acronym of the day (BAD) department

The Adept project is bringing some metrics and tools to help optimize energy-efficient use of parallel technologies. According the web site, "Adept builds on the expertise of software developers from high-performance computing (HPC) to exploit parallelism for performance, and on the expertise of Embedded systems engineers in managing energy usage. Adept is developing a tool that can guide software developers and help them to model and predict the power consumption and performance of parallel software and hardware."

Recently, Adapt released a benchmarks suite to help understand and measure power usage for HPC and embedded systems The benchmark suite consists of a wide range of benchmarks including both high-performance embedded and high-performance technical computing. The benchmarks are designed to characterize the efficiency (both in terms of performance and energy) of computer systems, from the hardware and system software stack to the compilers and programming models. More information about the benchmark suite can found on the EPCC Blog Page

Hopefully the ClusterMonkey crew will carve out some time to play with these tools and report back on their experiences.

Intel and Micron just announced a new type of memory that does not use transistors. Called 3D Xpoint memory, the new technology is reported to be 1,000 times faster in both read and write than NAND, as well ten times more dense with 1000 times more endurance. If this is true and the price is right a real disruption awaits the industry. The performance numbers are orders of magnitude over anything else (HP memristor where are you?).

Intel states that it not a phase-change memory process, a memristor technology, or a spin-transfer torque technique. Get more details here: What a New Class of Memory Means for Future Applications (The Platform).

Editors Note: The stories and articles have slowed to a trickle because of this: Hadoop 2 Quick-Start Guide. The book is now in production so more Monkey goodness shall be forthcoming.

Update: Intel has announced Optane branded drives and memory sticks built using Xpoint memory. Get more details from the Platform article: Intel Reveals Plans For Optane 3D XPoint Memory


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