I think it is well established that repeat COVID infections have an increasingly severe impact on your body.
It is also true that COVID can weaken your general immune system.
But that doesn’t mean that getting COVID won’t increase your immunity against a future COVID infection, temporarily at least, just the way the effectiveness of the vaccine is temporary.
A subscription model where you know the exact terms is less exploitative than a fixed cost where it’s not even clear if the upgrade is sticky or not (as is currently the case for Tesla.)
I don’t get the “it’s not a service” argument. It’s not relevant. There’s no ongoing cost with many software licenses either. It’s just a business model.
It’s exploitative if it is not sticky and isn’t clearly communicated. But it’s not relevant for the Mercedes case where it’s very clear that it’s a subscription.
For my personal case, it still doesn’t matter, of course.
My Model Y has a $2000 in-app purchase option to increase acceleration by something like 0.5s.
It’s not a subscription, but the idea is the same.
It doesn’t bother me one bit: I bought the car knowing full well that it wasn’t included, I don’t need it (the standard acceleration is already more than I ever had before), end of story.
Car engines have been under the control of software for decades now, with different products differing by the program. The only difference here is that there’s now the option to change the program over the air.
Nobody would have complained if Mercedes had offered 2 versions without the option to upgrade.
It would make zero difference, except, maybe, for the PCIe bandwidth test. Because all other tests were stressed interfaces that reside within the GPU silicon itself or between the GPU and the DRAM.
Even the numbers of PCIe bandwidth test might not change much, even it's trying to test medium size memory to memory block transfers.
I’d go dither than that and claim that almost everything of importance has checksums: Google Docs, Google Sheets, Git submissions, all my important web accounts, the traffic with my bank website and so forth.
When I look at my daily home computer usage, it’s remarkable how little I calculate on my local computer that’s actually with protecting.
Why pay for something I don’t need when there’s the option to not pay for it? - me
Just yesterday, I bought an extra 64GB for my home Linux PC. I absolutely couldn’t care less about it crashing or calculating the wrong result every blue moon (in practice: never), but I did choose the RAM sticks that were $10 cheaper.
That seems like a stretch? Isn’t the more obvious explanation that laptops with a real GPU are much more expensive and that the weaker, integrated GPUs are more than good enough for the vast majority of business use?
Today’s iGPUs are fast enough comfortable run plenty of games.
I have work provided high-end POS Dell Precision engineering laptop. It has an Nvidia discrete GPU, but I don’t think I’ve ever actually needed its power, and I’d gladly trade it for a laptop without…
Moving around data is indeed a major issue for any throughout oriented device. But for a gaming GPU, PCIe BW has never been an issue in any of the benchmarks that I’ve seen. (Those benchmarks artificially reduce the number of PCIe lanes.)
In fact, the 4000 series still has PCIe 4.
Moving data around for a GPU is about feeding the shader cores by the memory system. PCIe is way too slow to make that happen. That’s why a GPU has gigabytes of local RAM.
GP100 and GP GeForce has a different shared memory structure as well, so much so that GP100 was listed as having 30 SMs instead of 60 in some Nvidia presentations. But the base architecture (ISA, instruction delays, …) were the same.
It’s true tbat GA102 has double the FP32 units, but the way they works is very similar to the way SMs have 2x FP16 in that you need to go out of your way to benefit front them. Benchmark show this as well.
I like to think that Nvidia’s SM version nomenclature is a pretty good hint, but I guess it just boils down to personal opinion about what constitutes a base architecture.
The weird part is that this latency difference has to be due to a terrible MC design by AMD, because there's not a huge difference in latency between any of the current DRAM technologies: the interface between HBM and GDDR (and regular DDR) is different, but the underlying method of accessing the data is similar enough for the access latency to be very similar as well.
Turing is an evolution of Volta. In fact, in the CUDA slides of Turing, they mention explicitly that Turing shaders are binary compatible with Volta, and that's very clear from the whitepapers as well.
Ampere A100 and Ampere GeForce have the same core architecture as well.
The only differences are in HPC features (MIG, ECC), FP64, the beefiness of the tensor cores, and the lack of RTX cores on HPC units.
The jury is still out on Hopper vs Lovelace. Today's presentation definitely points to a similar difference as between A100 and Ampere GeForce.
It's more: the architectures are the same with some minor differences.
But what kind of latency are we talking about here?
CDNA has 16-wide SIMD units that retires 1 64-wide warp instruction every 4 clock cycles.
RDNA has a 32-wide SIMD unit that retires 1 32-wide warp every clock cycle. (It's uncanny how similar it to to Nvidia's Maxwell and Pascal architecture.)
Your 1/4 number makes me think that you're talking about a latency that has nothing to do with reads from memory, but with the rate at which instructions are retired? Or does it have to with the depth of the instruction pipeline? As long as there's sufficient occupancy, a latency difference of a few clock cycles shouldn't mean anything in the context of a thousand clock cycle latency for accessing DRAM?
It is also true that COVID can weaken your general immune system.
But that doesn’t mean that getting COVID won’t increase your immunity against a future COVID infection, temporarily at least, just the way the effectiveness of the vaccine is temporary.