81–90 of 169 posts
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Let's hope this isn't niagra again: it needs to have decent clock speeds as IPC is still worth something today. But yes, I totally agree, this is an exciting chip.
It's not, not only did AMD move from CMT (clustered multi-thread) design used in the previous Bulldozer microarchitecture, they now have an SMT (simultaneous multithreading) architecture allowing for 2 threads per core. By comparison, the performance of sparc substantially improved moving from the T1, T2 to T3+. The T1 used a round-robin policy to issue instructions from the next active thread each cycle, supporting…
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It's quite rare to find GPUs being used in genetics.
Is that because the workloads are fundamentally unsuitable for current GPU architectures or because no one has took a good stab at it yet? I know very little about computation genetics/biology but it sounds interesting.
(More than a decade ago, I struggled to / barely succeeded in building a Beowulf cluster; I am just amazed at how far both the hardware & the software tools have come..)
In other areas of comp bio though, GPUs I think are finding use. Protein folding, molecular dynamics. Also, with STORM & such: super resolution microscopy? I think increasingly, gpus will become important.
Also, whole cell simulations?
I think Naples is a very exciting development, because: - 1S/2S is obviously where the pie is. Few servers are 4S. - 8 DDR4 channels per socket is twice the memory bandwidth of 2011, and still more than LGA-36712312whateverthenumberwas - First x86 server platform with SHA1/2 acceleration - 128 PCIe lanes in a 1S system is unprecedented All in all Naples seems like a very interesting platform for throughput-intensive…
This one will be interesting. The current Ryzen (like most of the Intel desktop range) has two channels, but everyone has been benchmarking it against the i7-6900K because they both have eight cores. The i7-6900K is the workstation LGA 2011 with four channels. If the workstation Ryzen will have eight channels...
With how big these chips are getting, I wonder if the next iteration will have an HBM last-level cache on chip.
http://wccftech.com/amd-exascale-heterogeneous-processor-ehp...
I'd like to have that in the old project quantum package: http://wccftech.com/amd-project-quantum-not-dead-zen-cpu-veg...
That would be a TFLOPS level supercomputer on your desk.
I think Naples is a very exciting development, because: - 1S/2S is obviously where the pie is. Few servers are 4S. - 8 DDR4 channels per socket is twice the memory bandwidth of 2011, and still more than LGA-36712312whateverthenumberwas - First x86 server platform with SHA1/2 acceleration - 128 PCIe lanes in a 1S system is unprecedented All in all Naples seems like a very interesting platform for throughput-intensive…
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>And AMD should dump SHA1 acceleration in the next generation. The cost to have that on silicon is probably close to zero. If you think SHA1 is just going to magically disappear because you want it to, well, you'll be in for a SHA1 sized surprise. Our grandkids will still have SHA1 acceleration. >ARMv8 has had it for like 2-3 years now... Because ARM cores don't remotely have the CPU heft an Intel x86/64 chip has, so…
Why should it be dropped ? Isn't it just a hash function ?
Most uses of special instructions will check feature bits or CPU version, but not all will do so correctly.
(I'd say that the additional area cost of something like this is small, and the big cost of special instructions is reserving opcodes and feature bits)
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It's quite rare to find GPUs being used in genetics.
Is that because the workloads are fundamentally unsuitable for current GPU architectures or because no one has took a good stab at it yet? I know very little about computation genetics/biology but it sounds interesting.
On the other hand many of the bioinformatics software solve a specific scientific question and usually are written by people with mostly non-computational background. They use higher level languages such as Python/Perl/R and people often don't have the expertise or time to implement them for GPUs.
However now that machine learning and deep neural network approaches are being picked up by the field, the workloads might change a and also there are frameworks that make it easer to leverage GPUs (Tensorflow, etc)
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Is that because the workloads are fundamentally unsuitable for current GPU architectures or because no one has took a good stab at it yet? I know very little about computation genetics/biology but it sounds interesting.
I don't think it is because no one has tried it as much as the fact that the workloads need the cpu architecture / are not easily parallizable (as far as I understand). Comp bio in genetics is largely sequence alignment & search, which is still largely CPU / memory bound; but I don't understand programming enough to speculate if development in algorithms will allow GPUs to be used because the problem itself is not pa…
You are also right that some of the comp bio areas (CryoEM, protein folding, molecular dynamics) are well suited for GPUs