AI
Tuning Up Knights Landing For Gene Sequencing
The Smith-Waterman algorithm has become a linchpin in the rapidly expanding world of bioinformatics, the go-to computational model for DNA sequencing and local sequence alignments. …
Apache Kafka Gives Large-Scale Image Processing a Boost
The digital world is becoming ever more visual. From webcams and drones to closed-circuit television and high-resolution satellites, the number of images created on a daily basis is increasing and in many cases, these images need to be processed in real- or near-real-time. …
Google Expands Enterprise Cloud With Machine Learning
Google’s Cloud Platform is the relative newcomer on the public cloud block, and has a way to go before before it is in the same competitive sphere as Amazon Web Services and Microsoft Azure, both of which deliver a broader and deeper range of offerings and larger infrastructures. …
An Early Look at Startup Graphcore’s Deep Learning Chip
As a thought exercise, let’s consider neural networks as massive graphs and begin considering the CPU as a passive slave to some higher order processor—one that can sling itself across multiple points on an ever-expanding network of connections feeding into itself, training, inferencing, and splitting off into multiple models on the same architecture. …
Stanford’s TETRIS Clears Blocks for 3D Memory Based Deep Learning
The need for speed to process neural networks is far less a matter of processor capabilities and much more a function of memory bandwidth. …
Japan to Unveil Pascal GPU-Based AI Supercomputer
A shared appetite for high performance computing hardware and frameworks is pushing both supercomputing and deep learning into the same territory. …
Looking Down The Long Enterprise Road With Hadoop
Just five years ago, the infrastructure space was awash in stories about the capabilities cooked into the Hadoop platform—something that was, even then, only a few pieces of code cobbled onto the core HDFS distributed storage with MapReduce serving as the processing engine for analytics at scale. …
Promises, Challenges Ahead for Near-Memory, In-Memory Processing
The idea of bringing compute and memory functions in computers closer together physically within the systems to accelerate the processing of data is not a new one. …
Current Trends in Tools for Large-Scale Machine Learning
During the past decade, enterprises have begun using machine learning (ML) to collect and analyze large amounts of data to obtain a competitive advantage. …