AI
GOAI: Keeping Databases, Analytics, And Machine Learning All On The GPU
Moving data is the biggest problem in computing, and probably has been since there was data processing if we really want to be honest about it. …
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AI
A Dive into Deep Learning Chip Startup Graphcore’s Software Stack
The last two years have delivered a new wave of deep learning architectures designed specifically for tackling both training and inference sides of neural networks. …
AI
Crunching Machine Learning And Databases Together On GPUs
While it is always best to have the right tool for the job, it is better still if a tool can be used by multiple jobs and therefore have its utilization be higher than it might otherwise be. …
AI
Generalizing a Hardware, Software Platform for Industrial AI
Industrial companies have replaced people with machines, systems analysts with simulations, and now the simulations themselves could be outpaced by machine learning—albeit with a human in the loop, at the beginning at least. …
AI
Cluster Management for Distributed Machine Learning at Scale
Over the last couple of decades, those looking for a cluster management platform faced no shortage of choices. …
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The Next Battleground for Deep Learning Performance
The frameworks are in place, the hardware infrastructure is robust, but what has been keeping machine learning performance at bay has far less to do with the system-level capabilities and more to do with intense model optimization. …
AI
A Trillion Edge Graph on a Single Accelerated Node
Efficiently and quickly chewing through one trillion edges of a complex graph is no longer in itself a standalone achievement, but doing so on a single node, albeit with some acceleration and ultra-fast storage, is definitely worth noting. …
AI
Pushing A Trillion Row Database With GPU Acceleration
There is an arms race in the nascent market for GPU-accelerated databases, and the winner will be the one that can scale to the largest datasets while also providing the most compatibility with industry-standard SQL. …
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Escher Erases Batching Lines for Efficient FPGA Deep Learning
Aside from the massive parallelism available in modern FPGAs, there are other two other key reasons why reconfigurable hardware is finding a fit in neural network processing in both training and inference. …
AI
Taking the Heavy Lifting Out of TensorFlow at Extreme Scale
There is no real middle ground when it comes to TensorFlow use cases. …