7K views · Nov 17, 2016 · 42:13 · 1 HN recommender · 3 mentions · discussed 2016–2018
Dragan Djuric presents Clojure libraries for GPU and numerical computing, including ClojureCL, ClojureCUDA and Neanderthal. He shows how a dynamic REPL workflow can be combined with low-level hardware acceleration for machine learning and data science.
Why Hacker News points people here: HN commenters point to it as the talk by the author of Neanderthal and the Clojure GPU libraries, including comparisons against the JVM and CPU.
“Watch his talk from EuroClojure 2016 in Bratislava”HN comment, 2017
“Watch his talk from EuroClojure 2016 in Bratislava”
Talk by author of ClojureCUDA, ClojureCL and Neanderthal
ClojureCUDA, ClojureCL (as in OpenCL) and Neanderthal are libraries developed by Dragan Djuric. Watch his talk from EuroClojure 2016 in Bratislava: this video
“Data crunching on GPUs is a LISP strength.”
Clojure GPU computing talk
Data crunching on GPUs is a LISP strength. It runs code orders of magnitude faster than say python or c++. this video
“After watching your talk on youtube”
Talk compares GPU implementation against JVM and CPU
ClojureCL looks quite awesome, it looks like it takes all of the boiler plate out in comparison to C but I am very interested in the overhead. I tried aparapi for my bachelor thesis and for larger problems it seemed to have quite the overhead that the normal kernel did not have (several years ago). After watching your talk on youtube [1], I saw you compared the GPU implementation to the JVM and other CPU implementations in Clojure. Do you have any numbers for the same kernel being called from a native C openCL application? [1] this video
