hUMA has come with a weapon to slay the memory latency dragon

Subject: General Tech | April 30, 2013 - 01:23 PM |
Tagged: Steamroller, piledriver, Kaveri, Kabini, hUMA, hsa, GCN, bulldozer, APU, amd

AMD may have united GPU and CPU into the APU but one hurdle had remained until now, the the non-uniformity of memory access between the two processors.  Today we learned about one of the first successful HAS projects called Heterogeneous Uniform Memory Access, aka hUMA, which will appear in the upcoming Kaveri chip family.   The use of this new technology will allow the on-die CPU and GPU to access the same memory pool, both physical and virtual and any data passed between the two processors will remain coherent.  As The Tech Report mentions in their overview hUMA will not provide as much of a benefit to discrete GPUs, while they will be able to share address space the widely differing clock speeds between GDDR5 and DDR3 prevent unification to the level of an APU.

Make sure to read Josh's take as well so you can keep up with him on the Podcast.

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"At the Fusion Developer Summit last June, AMD CTO Mark Papermaster teased Kaveri, AMD's next-generation APU due later this year. Among other things, Papermaster revealed that Kaveri will be based on the Steamroller architecture and that it will be the first AMD APU with fully shared memory.

Last week, AMD shed some more light on Kaveri's uniform memory architecture, which now has a snazzy marketing name: heterogeneous uniform memory access, or hUMA for short."

Here is some more Tech News from around the web:

Tech Talk

April 30, 2013 | 02:45 PM - Posted by Anonymous (not verified)

Dragonlance reference?

April 30, 2013 | 06:56 PM - Posted by Jeremy Hellstrom

Had to be done.

May 1, 2013 | 09:17 AM - Posted by razor512

Why cant they just seamlessly combine the CPU and GPU, eg have the GPU show up as just another CPU core where either the CPU or some driver will dynamically transfer processes to the GPU if it detects a specific set of instructions or tasks that the GPU can handle more effectively, that way applications will not have to be programmed specifically for the GPU using some API or other steep learning curve process.

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