Available on academia.edu accepted in ACM Computing Surveys 2015
Researchers are now moving away from "CPU vs GPU debate" towards "CPU-GPU collaborative computing" and this paper surveys nearly 200 papers related to such an approach. Especially Table 7 of this paper organizes the works based on their field, e.g. Maths, Physics, Chemistry, data mining, image processing, bioinformatics. This survey discusses both fused CPU-GPU chips and discrete GPU systems.
I noticed related posts in this forum (e.g. http://community.wolfram.com/groups/-/m/t/253219) and hence, thought this paper may be interesting to forum members.
While this paper does not directly deal with any specific product or technology, it presents an interesting idea that scientific computation can be accelerated not merely by blindly offloading computation-intensive tasks to GPUs (as is the present mindset of research community), but by intelligent use of both CPU and GPU together, since both have unique strengths.
As shown by these articles https://reference.wolfram.com/language/example/SpeedUpComputationsWithParallelGPUComputing.html and https://reference.wolfram.com/language/guide/GPUComputing.html, Wolfram language seeks acceleration using GPU. So, based on the ideas presented by this survey, even better acceleration may be possible by using computation power of both CPU and GPU and performing load-balancing or pipelining in them. This also avoids CPU idling and enables boosting even serial applications which cannot benefit from GPUs.
Still, you may kindly decide about relevance of this paper to the forum.
Is there anything about Wolfram Language or Technologies in the paper?