Everyone has a dream. But sometimes there’s a gap between where we are and where we want to be. True, there are some people who can bridge that gap easily, on their own, but all of us need a little help at some point. A little boost. An accountability partner. A Snooze Squad. In each episode, the Snooze Squad will strategize an action plan for people to face their fears. Guests will transform their own perception of their potential and walk away a few inches closer to who they want to become ...
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Konten disediakan oleh tele-TASK and Dr. Thorsten Papenbrock. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh tele-TASK and Dr. Thorsten Papenbrock atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang dijelaskan di sini https://id.player.fm/legal.
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Distributed Data Analytics (WT 2017/18) - tele-TASK
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Konten disediakan oleh tele-TASK and Dr. Thorsten Papenbrock. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh tele-TASK and Dr. Thorsten Papenbrock atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang dijelaskan di sini https://id.player.fm/legal.
The free lunch is over! Computer systems up until the turn of the century became constantly faster without any particular effort simply because the hardware they were running on increased its clock speed with every new release. This trend has changed and today's CPUs stall at around 3 GHz. The size of modern computer systems in terms of contained transistors (cores in CPUs/GPUs, CPUs/GPUs in compute nodes, compute nodes in clusters), however, still increases constantly. This caused a paradigm shift in writing software: instead of optimizing code for a single thread, applications now need to solve their given tasks in parallel in order to expect noticeable performance gains. Distributed computing, i.e., the distribution of work on (potentially) physically isolated compute nodes is the most extreme method of parallelization. Big Data Analytics is a multi-million dollar market that grows constantly! Data and the ability to control and use it is the most valuable ability of today's computer systems. Because data volumes grow so rapidly and with them the complexity of questions they should answer, data analytics, i.e., the ability of extracting any kind of information from the data becomes increasingly difficult. As data analytics systems cannot hope for their hardware getting any faster to cope with performance problems, they need to embrace new software trends that let their performance scale with the still increasing number of processing elements. In this lecture, we take a look a various technologies involved in building distributed, data-intensive systems. We discuss theoretical concepts (data models, encoding, replication, ...) as well as some of their practical implementations (Akka, MapReduce, Spark, ...). Since workload distribution is a concept which is useful for many applications, we focus in particular on data analytics.
…
continue reading
14 episode
Tandai semua (belum/sudah) diputar ...
Beranda serial•Feed
Manage series 1914092
Konten disediakan oleh tele-TASK and Dr. Thorsten Papenbrock. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh tele-TASK and Dr. Thorsten Papenbrock atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang dijelaskan di sini https://id.player.fm/legal.
The free lunch is over! Computer systems up until the turn of the century became constantly faster without any particular effort simply because the hardware they were running on increased its clock speed with every new release. This trend has changed and today's CPUs stall at around 3 GHz. The size of modern computer systems in terms of contained transistors (cores in CPUs/GPUs, CPUs/GPUs in compute nodes, compute nodes in clusters), however, still increases constantly. This caused a paradigm shift in writing software: instead of optimizing code for a single thread, applications now need to solve their given tasks in parallel in order to expect noticeable performance gains. Distributed computing, i.e., the distribution of work on (potentially) physically isolated compute nodes is the most extreme method of parallelization. Big Data Analytics is a multi-million dollar market that grows constantly! Data and the ability to control and use it is the most valuable ability of today's computer systems. Because data volumes grow so rapidly and with them the complexity of questions they should answer, data analytics, i.e., the ability of extracting any kind of information from the data becomes increasingly difficult. As data analytics systems cannot hope for their hardware getting any faster to cope with performance problems, they need to embrace new software trends that let their performance scale with the still increasing number of processing elements. In this lecture, we take a look a various technologies involved in building distributed, data-intensive systems. We discuss theoretical concepts (data models, encoding, replication, ...) as well as some of their practical implementations (Akka, MapReduce, Spark, ...). Since workload distribution is a concept which is useful for many applications, we focus in particular on data analytics.
…
continue reading
14 episode
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