The race to become the quickest and most efficient library is now in full flight. IBM claims that performing machine learning tasks on it's POWER servers is an incredible 46 times quicker than on TensorFlow used in Google Cloud. Earlier this year, a Google software engineer wrote a blog post on how they used Google Cloud Machine Learning and TensorFlow for solving click prediction problems. They trained their deep neural network model "to predict display ad clicks on Criteo Labs clicks logs. These logs are over 1TB in size and include feature values and click feedback from millions of display ads".

Analysis IBM boasts that machine learning is not just quicker on its POWER servers than on TensorFlow in the Google Cloud, it's 46 times quicker. Back in February Google software engineer Andreas Sterbenz wrote about using Google Cloud Machine Learning and TensorFlow on click prediction for large-scale advertising and recommendation scenarios. He trained a model to predict display ad clicks on Criteo Labs clicks logs, which are over 1TB in size and contain feature values and click feedback from millions of display ads. Data pre-processing (60 minutes) was followed by the actual learning, using 60 worker machines and 29 parameter machines for training. The model took 70 minutes to train, with an evaluation loss of 0.1293.

Analysis IBM boasts that machine learning is not just quicker on its POWER servers than on TensorFlow in the Google Cloud, it's 46 times quicker. Back in February Google software engineer Andreas Sterbenz wrote about using Google Cloud Machine Learning and TensorFlow on click prediction for large-scale advertising and recommendation scenarios. He trained a model to predict display ad clicks on Criteo Labs clicks logs, which are over 1TB in size and contain feature values and click feedback from millions of display ads. Data pre-processing (60 minutes) was followed by the actual learning, using 60 worker machines and 29 parameter machines for training. The model took 70 minutes to train, with an evaluation loss of 0.1293.

IBM is hosing its annual THINK conference to packed halls in Las Vegas this week. Given how important its cloud business has become to its bottom line, it's no surprise that this event features its fair share of cloud news. This comes a day after Google also confirmed that it is using these processors in its data centers, too. These servers are designed around the recently launched Power9 RISC processor (which are themselves the latest generation of the PowerPC processors Apple once used) and Nvidia Tesla V100 GPUs. Thanks to their use of the high-speed NVLink interface, these machines are especially powerful when it comes to training machine learning models.

IBM has unveiled next-generation Power Systems Servers incorporating its newly designed Power9 processor, built specifically for compute-intensive AI workloads. Tthe new Power9 systems are capable of improving the training times of deep learning frameworks by nearly 4-times, allowing enterprises to build more accurate AI applications, faster. The new Power9 -based AC922 Power Systems are the first to embed PCI-Express 4.0, next-generation NVIDIA NVLink and OpenCAPI, which combined can accelerate data The system was designed to drive demonstrable performance improvements across popular AI frameworks such as Chainer, TensorFlow and Caffe, as well as accelerated databases such as Kinetica. As a result, data scientists can build applications faster, ranging from deep learning insights in scientific research, real-time fraud detection and credit risk analysis. Power9 is at the heart of the soon-to-be most powerful data-intensive supercomputers in the world, the US Department of Energy's "Summit" and "Sierra" supercomputers, and has been tapped by Google.

Computational workloads are growing, and processors, memory, and storage are getting faster at a blazing pace. Emerging technologies could leave computers choking for bandwidth. The potential chokepoint worries companies like Google, IBM, Samsung, and Dell, which are moving to remedy the problem. New specifications from two new consortia will bring data unprecedented boosts in data transfer speeds to computers as early as next year. OpenCAPI Consortium's connector specification will bring significant bandwidth improvements inside computers.

Computational workloads are growing, and processors, memory, and storage are getting faster at a blazing pace. Emerging technologies could leave computers choking for bandwidth. The potential chokepoint worries companies like Google, IBM, Samsung, and Dell, which are moving to remedy the problem. New specifications from two new consortia will bring data unprecedented boosts in data transfer speeds to computers as early as next year. OpenCAPI Consortium's connector specification will bring significant bandwidth improvements inside computers.