Nvidia tesla m2070 bitcoin
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As for the algorhytm: So by design the hashrate shoudl scale with memory bandwidth. The high end Nvidia cards are only high end because they run specific CUDA driver features that are not accessible to the commodity cards which use the same chips.
It looks like you're new here. If you want to get involved, click one of these buttons! August in Mining. Building my GPU rig. Still haven't settled on a graphics card just yet. However, in terms of a rough comparison, I did some googling to see how they compare CUDA cores vs Stream processors I nvidia tesla m2070 bitcoin they don't directly compare 1. More memory, slower and less bandwidth, but a higher clock speed? There's still two shift instructions, one of which might require many iterations to shift.
AMD fires back though with a single rotate-wherever bit align instruction. Both devices are, of course, using CUDA 2.
I nvidia tesla m2070 bitcoin experiment to see just what it can nvidia tesla m2070 bitcoin. Returns within 30 days for the tesla are accepted if it doesn't work out What do you miners think?
Deal or no deal? I personally have a mix of r9 sr9 s and a single r9 and about six x's I would have liked to the go the Nvidia route due to the lower power use but their higer end cards are just to expensive. Nvidia tesla m2070 bitcoin the R9s run hot and nvidia tesla m2070 bitcoin throttle down anyways. I went th R9 route becase I already had a bunch of those and I only don't want to nvidia tesla m2070 bitcoin motherboards worth of Nvidia cards for the same hashrate of AMD cards using only 2 motherboards.
Suitable also for games. Genoil 0xebbffdab3dfb4d Member Posts: August edited August You could try Tesla M on an Amazon EC2 g1 instance type, but I don't think it will perform very well set against its nvidia tesla m2070 bitcoin. My guess is that AMD still wins over NVidia because of faster integer ops as cards with similar memory bandwidth perform quite a bit worse.
This means the NVidia cards cannot fully hide memory latency because of slower integer ops. Don't have any comparison on AMD. I would to try out ethashing on a Tesla K80 though. My request to NVidia for a test drive was denied however Here's some graphs from NSight to illustrate the bandwidth dependency: And this gives an idea about distribution of ALU workload.
XOR fall within the Logical Ops. Shuffle has nothing to do with shifting btw. Other than normal GPU memory reads, that usually consist of large chunks of coalesced data textures, verticesethash sequentically picks tiny fractions bits from random locations in the DAG.
It might therefore not be able to optimally use to ultra-wide bit bus and then suffers from the relatively low clocks. You know what I just realized? And, of course, the K still has many more cores than the M If that's the case, I'll save my money for a Sign In or Register to comment.