Program Mission and Vision
The programs are designed so that participants acquire skills and knowledge on production related to the actual production process found in their production line through an entire module covering several levels of employee groups from Line Operator, Line Leader and even Supervisor. In addition, the main goal of the program is to improve skills and knowledge so that participants are more competitive in their respective organizations.
Dear PCEHMA- Group 2 Students,
Here is the link for your Test 2- Hands-on Basic Electronics Components.
CLICK BELOW
LINK
https://forms.gle/mCG13d8RM6VieBk29
1st Attempt will only be counted.
Best of Luck!!
HPC Challenge is a benchmark suite that measures a range memory access patterns. Source code repository of the benchmark is located at HPCC SourceForge and HPCC@BitBucket pages.
The HPC Challenge benchmark consists of basically 7 tests:
HPCG, http://hpcg-benchmark.org, is a new metric for ranking HPC (high performance computing) systems. HPCG is intended as a complement to the High Performance LINPACK (HPL) benchmark, currently used to rank the TOP500 computing systems. Current software is only for NVIDIA GPUs.
LINPACK is the historical benchmark for supercomputers. Intel provides pre-compiled binaries for macOS,https://software.intel.com/en-us/articles/intel-mkl-benchmarks-suite.
With the power of Mac’s increasing, it would be interesting to analyse the performance. The scalability of HPCG benchmark using MapReduce implementation using fine grained parallelism on Hadoop cluster is worth to work upon.
Even though the Xeon processor has become the default engine for most kinds of compute in the datacenter, it is by no means to only option that is available to large enterprises that can afford to indulge in different kinds of systems because they do not have to homogenize their systems as hyperscalers must if they are to keep their IT costs in check.
Sometimes, there are benefits to being smaller, and the ability to pick point solutions that are good for a specific job is one of them. This has been the hallmark of the high-end of computing since data processing systems were first developed many decades ago, and it continues to be the case with supercomputing and other exotic types of infrastructure at large and sophisticated enterprises.
It is with this in mind that we contemplate the new “Sonoma” S7 processor, which Oracle unveiled last summer and which is at the heart of the new Sparc S7 systems that made their initial debut in late June. Like other alternatives to the Xeon, the Sparc S7 processor has to demonstrate performance and value advantages compared to the Xeon – it is not sufficient to be compatible with prior Sparc processors and show price/performance improvements against those earlier generations of Sparcs. The Xeon processor so utterly dominates the modern datacenter and is such a safe choice that Sparc, Power, or ARM processors have to meet or beat it if they have any hope of getting traction.
According to the benchmarks that Oracle has put together for the Sparc S7 systems, these machines can compete effectively against modern Xeon E5 processors, particularly for workloads that require relatively brawny cores and high clock speeds and perhaps especially for software that is priced per core, as Oracle’s own database and middleware software is.
Given Oracle’s key business of peddling relational database software – it has over 310,000 customers worldwide using its eponymous database software – you would expect for the Sparc S7 processors aimed at two-socket machines and M7 processors aimed at larger NUMA machines would be tricked out to accelerate databases and to offer very competitive performance. And according to the benchmark tests that Oracle has run, this is the case. But Oracle is also interested in running other workloads on the S7 systems, and has run benchmarks that show the machines to be competitive running Java application, analytics, and NoSQL tests.
Oracle’s desire is to position the S7 systems directly against Xeon systems for database workloads and to do more work with fewer cores, which plays into its strategy of lowering the cost of its software to help promote its hardware.
Oracle uses a processor core scaling factor to adjust its database pricing based on core counts and architecture, with IBM Power and Intel Itanium processors having to pay full price per core but modern Xeon E5 chips as well as the most recent Sparc T, S, and M series chips from Oracle have a 0.5 scaling factor on the core counts, which means they get a 50 percent discount for software licenses. (You can see the scaling factors, which were first introduced in 2009, at this link.) With the scaling factors being the same on the Xeon and Sparc S7 processors, the odds are even here, but compared to chips with brawnier cores, like the Power8 chip from IBM, Oracle gives its own S7 and M7 platforms a software pricing advantage because of the core scaling factor.
Read the complete article by Timothy Prickett Morgan
https://www.nextplatform.com/2016/07/21/stacking-oracle-s7-intel-xeon/
Back in 2009, Hadoop was the next big thing in market. I got acquainted to Hadoop during my undergraduate final year project. I had enormous interest in databases and ER modelling stuffs. Therefore, for my project I wanted to go with something related to these topics and hence I approached one of my favorite lecturer in university who was the “guru” of High Performance Computing and database field and then my journey began with Hadoop.
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. As the World Wide Web grew in the late 1900s and early 2000s, search engines and indexes were created to help locate relevant information amid the text-based content. In the early years, search results were returned by humans. But as the web grew from dozens to millions of pages, automation was needed. Web crawlers were created, many as university-led research projects, and search engine start-ups took off (Yahoo, AltaVista, etc.).
One such project was an open-source web search engine called Nutch – the brainchild of Doug Cutting and Mike Cafarella. They wanted to return web search results faster by distributing data and calculations across different computers so multiple tasks could be accomplished simultaneously. During this time, another search engine project called Google was in progress. It was based on the same concept – storing and processing data in a distributed, automated way so that relevant web search results could be returned faster.
In 2006, Cutting joined Yahoo and took with him the Nutch project as well as ideas based on Google’s early work with automating distributed data storage and processing. The Nutch project was divided – the web crawler portion remained as Nutch and the distributed computing and processing portion became Hadoop (named after Cutting’s son’s toy elephant). In 2008, Yahoo released Hadoop as an open-source project. Today, Hadoop’s framework and ecosystem of technologies are managed and maintained by the non-profit Apache Software Foundation (ASF), a global community of software developers and contributors.
(For further reference please log on to https://www.sas.com/en_us/insights/big-data/hadoop.html)
Computing systems in the upcoming decade will be dealing with millions of processors and ‘n’ number of threads. If we want to cope up with the extensive scientific needs and demands, then an increasing computing power has to be adopted. Further performance gains could be achieved through task parallelization which could help us to take the big jump in this advanced technological era. Big giants like Intel has its own initiatives to deal with such issues for which they have setup Intel® Parallel Computing Centers (Intel® PCC). To find out more, please visit:
https://software.intel.com/en-us/ipcc
Parallelization of task is considered to be a huge challenge for future extreme-scale computing system. Sophisticated parallel computing system necessitates solving the bus contention in a most efficient manner with high computation rate. The major challenge to deal with is the achievement of high CPU core usage through increased task parallelism by keeping moderate bus bandwidth allocation. In order to tackle the aforesaid problems, a novel arbitration technique, known as Parallel Adaptive Arbitration (PAA) has been proposed for the masters designed according to the traffic behaviour of the data flow. These masters are implemented using a synthetic benchmark program that measures sustainable memory bandwidth and the corresponding computational rate. The proposed arbitration technique is a strong case in favour of fair bandwidth optimization and high CPU utilization, as it consumes the processor cores up to 77% through high degree of task parallelization and also reduces bandwidth fluctuation.
To find out more regarding PAA, follow the link below:
http://www.sciencedirect.com/science/article/pii/S0045790615002815
If you are unable to view the article, then you can contact me.
This was my first visit to the University of Amsterdam in Science Park and I never knew that that SNET Compiler would be so alluring. I would like to thank Dr. Clemens Grelck of Information Science School (University of Amsterdam) for introducing me to S-NET.
Nowadays, it’s a great challenge for the programmers to design fully asynchronous nodes which runs in parallel. S-NET is something which works on the principle of asynchronous nodes. I would say that these nodes are 90% asynchronous. S-NET is designed to be a coordination language for asynchronous stream processing. The language achieves a near-complete separation between the application code, written in any conventional programming language, and the coordination/communication code written in S-Net. S-NET involved “Box” programming where each box is programmed separately and is then connected to other boxes using sequential pipelining. S-NET has got its own libraries and semantics. To find out more, please follow the below link.
https://staff.fnwi.uva.nl/c.u.grelck/publications/GrelSchoShafIJPP10.pdf