A million core RISC CPU

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At the recent HiPEAC conference in Stockholm, Andreas Olofsson presented a path to the world’s first million core RISC microprocessor. Slides (PDF) —- Andreas Olofsson, “How to build a million core processor”, MULTIPROG: 2017 International Workshop on Programmability and Architectures for Heterogeneous Multicores  

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An open source 1024-core Epiphany Simulator

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A couple of years ago, Ola Jeppsson started working on a 1024-core simulator as part of his Master’s Thesis at Chalmer’s University in Sweden. (advisor, Sally McKee). He picked up the project again in 2016 as part of the validation process of the 1024-core Epiphany-V. Debugging with the simulator is an order of magnitude easier than with hardware, so you should…

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Parabuntu 2016.11 Release

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Thanks to another heroic integration effort from Ola Jeppsson, we now have a much improved Parallella Linux Distribution based on Ubuntu 15.04. (Note the name change from “pubuntu” to “parabuntu”) Here’s a selection of some of the pre-installed features: HDMI improvements (Thanks to Peter Saunderson and Ola Jeppsson) GDB Epiphany multicore support (Thanks to Pedro Alvez) EPYTHON parallel programming framework…

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Task farms on the Epiphany

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Splitting problems up into tasks and running these concurrently over a number of cores is a popular approach to parallelism. In recent years this has becoming more and more popular and is seen as one of the ways in which parallel codes might be written for future machines with very large numbers of processing cores. In this tutorial we are…

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Epiphany-V: A 1024-core 64-bit RISC processor

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I am happy to report that we have successfully taped out a 1024-core Epiphany-V RISC processor chip at 16nm.  The chip has 4.5 Billion transistors, which is 36% more transistors than Apple’s latest 4 core A10 processor at roughly the same die size. Compared to leading HPC processors, the chip demonstrates an 80x advantage in processor density and a 3.6x advantage in memory…

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Pipelines on the Epiphany

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In the previous tutorial (available here) we looked at splitting a problem up geometrically. Driven by the decomposition of the data, different parts of the problem ran on different Epiphany cores with these cores often needing to communuicate when a neighbouring value held on another core was required. Whilst geometric decomposition is a very common approach not all problems are…

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Geometric decomposition with the Epiphany

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In the previous tutorial (here) we concentrated on different ways to pass messages between cores which is one of the core mechanisms of parallelism. We saw that messages can be point to point, where only two cores are involved, or collective where every core is involved. The forms of communication that you select depends upon the problem you are trying…

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