Can my code be accelerated by an RPU? Part 4: is the RPU for me?
PART 4: Seven top tech considerations
In our previous posts, we introduced you to Quside’s Randomness Processing Unit (RPU), outlined how to use it to accelerate your stochastic loads, and showcased different capabilities for which an RPU seems the perfect choice for your infrastructure.
Suppose you’re considering using Quside’s Randomness Processing Unit (RPU) to accelerate your stochastic and randomized algorithms. In that case, we’d like to use this post to help you further by describing the primary items for which using an RPU could be differential concerning other hardware accelerators.
Considering these points, you can use them as a guideline to determine whether the RPU fits your needs and, if so, which optimizations and techniques are most appropriate for your algorithm.
José Ramon Martínez
Leader of the computing activities
He got his BSc in Physics from the UCM (Madrid); his MSc in Photonics from the UPC-UB-UAB (Barcelona); and his Ph.D. in Photonics from ICFO, where he worked at the Nanophotonics Theory Group. His research focused on Computational Physics in Nanophotonic systems, publishing 10+ articles in high-profile journals and developing various advanced high-performance computing systems.
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