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posted by Fnord666 on Sunday June 23 2019, @06:47AM   Printer-friendly
from the when-in-rome... dept.

Submitted via IRC for Bytram

7nm AMD EPYC "Rome" CPU w/ 64C/128T to Cost $8K (56 Core Intel Xeon: $25K-50K)

Yesterday, we shared the core and thread counts of AMD's Zen 2 based Epyc lineup, with the lowest-end chip going as low as 8 cores while the top-end 7742 boasting 64 and double the threads. Today, the prices of these server parts have also surfaced, and it seems like they are going to be quite a bit cheaper than the competing Intel Xeon Platinum processors.

The top-end Epyc 7742 with a TDP of 225W (128 threads @ 3.4GHz) is said to sell for a bit less than $8K, while the lower clocked 7702 and 7702P (single-socket) are going to cost $7,215 and $4,955 (just) respectively. That's quite impressive, you're getting 64 Zen 2 cores for just $5,000, while on the other hand Intel's 28-core Xeon Platinum 8280 costs a whopping $18K and is half as powerful.


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  • (Score: 2) by takyon on Monday June 24 2019, @10:37AM (1 child)

    by takyon (881) <reversethis-{gro ... s} {ta} {noykat}> on Monday June 24 2019, @10:37AM (#859302) Journal

    I thought the same exact thing, but:

    https://en.wikipedia.org/wiki/Quantum_algorithm [wikipedia.org]

    Although all classical algorithms can also be performed on a quantum computer

    With the caveat that this would not apply to an annealer like D-Wave.

    If quantum computers running classical code turns out to be slow and impractical, I think you could still see applications for quantum computing on home computers, such as simulating real world systems within open world video games, or machine learning. If a quantum computer can be done near room temperature, without significant cooling, you could see it integrated onto a smartphone SoC or as an add-on card for desktops. Make it available, and people will figure out what to do with it.

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  • (Score: 2) by bzipitidoo on Monday June 24 2019, @12:56PM

    by bzipitidoo (4388) on Monday June 24 2019, @12:56PM (#859327) Journal

    Not to be facetious, but there's a lot of uncertainty around quantum computing. We don't even have a firm grasp of just what problems they can solve quickly that classical computers cannot, and we won't, until the famous question of whether P!=NP is solved. Most people strongly suspect that P!=NP, but if somehow it should turn out the opposite, that P=NP, then quantum computing may be of no value. So far, it is thought that BQP, the problems that a quantum computer can solve within a bounded amount of error in polynomial time, lies somewhere between P and NP, that is, that P is a subset of BQP, which is a subset of NP.

    In the efforts to move closer to solving whether P!=NP, researchers have come up with an awful lot of problem classifications. Fro instance, there's RP, the set of problems that can be solved in polynomial time with a randomized algorithm. RP is also somewhere between P and NP. RP might be equal to P. Whether it contains BQP or BQP contains it, or neither, is not known. Primality testing was known to be in RP, until recently when someone discovered a deterministic way to test for primality, placing that problem firmly in P.