4 ms·
These benchmarks seem primarily to demonstrate that for “straight line” computationally intensive algorithms, hyperthreading doesn’t buy you very much—in my exp
by twoodfin 6y ago
These benchmarks seem primarily to demonstrate that for “straight line” computationally intensive algorithms, hyperthreading doesn’t buy you very much—in my experience something like 20% additional throughput. In those scenarios, all else being equal, 64 cores worth of compute will have a big advantage over 36.
- comex 6y agoOr to put it more strongly: The multi-core comparison makes no sense. If the goal is to compare performance per core, as a way to evaluate CPU efficiency in the abstract, then they should be comparing equal numbers of physical cores, not logical cores. Such a comparison does miss some practical benefits of the ARM instances. The ARM instances seemingly have more physical cores available on a single AWS instance – but “performance on a single instance” would be a different comparison, one where the Intel instances shouldn’t be limited to 64 threads. And the ARM instances may have a cost advantage (I haven’t checked), but performance for equal cost would again be a different comparison.
- jsnell 6y agoI agree that the article should at least have mentioned the cost, to allow for comparison on a more meaningful basis than perf per core. Though in the end that makes things look worse for Intel. The c5.18xlarge (Xeon) is $3.06/hour, m6g.16xlarge (ARM) is $2.464/hour.
- ajross 6y agoThat's true, but it's also true that "straight line" algorithms that don't involve branching pipeline stalls tend to be straightforwardly parallelizable, in which case a scalar CPU isn't the right hardware to be using in the first place and you want to be comparing vector FPU and/or GPU implementations.