intel

来源： https://stackoverflow.com/questions/61980149/can-the-simple-decoders-in-recent-intel-microarchitectures-handle-all-1-%c2%b5op-inst

来源： https://stackoverflow.com/questions/57670764/different-inter-core-latency-measured-on-two-identical-skylake-xeon-gold-6154-sy

来源： https://stackoverflow.com/questions/58741806/is-there-any-way-to-write-for-intel-cpu-direct-core-to-core-communication-code

来源： https://stackoverflow.com/questions/58741806/is-there-any-way-to-write-for-intel-cpu-direct-core-to-core-communication-code

来源： https://stackoverflow.com/questions/25696039/intercept-rdtsc-instruction-from-guest-vm-userspace-in-kvm

来源： https://stackoverflow.com/questions/25696039/intercept-rdtsc-instruction-from-guest-vm-userspace-in-kvm

问题 Recently, I found a need to explore the Intel DAAL MKL for Data Science and was having difficulties finding the proper installations for a working environment in one location. After several days and trial and failures, I was able to reach a final installation process that I think would be beneficial to all the other Data Scientist enthusiast who are looking to get started with their Data Science adventures, utilizing Visual Studio Code or JupyterLab. Posted below are my recommended steps to

问题 Recently, I found a need to explore the Intel DAAL MKL for Data Science and was having difficulties finding the proper installations for a working environment in one location. After several days and trial and failures, I was able to reach a final installation process that I think would be beneficial to all the other Data Scientist enthusiast who are looking to get started with their Data Science adventures, utilizing Visual Studio Code or JupyterLab. Posted below are my recommended steps to

问题 I know that Numpy can use different backends like OpenBLAS or MKL. I have also read that MKL is heavily optimized for Intel, so usually people suggest to use OpenBLAS on AMD, right? I use the following test code: import numpy as np def testfunc(x): np.random.seed(x) X = np.random.randn(2000, 4000) np.linalg.eigh(X @ X.T) %timeit testfunc(0) I have tested this code using different CPUs: On Intel Xeon E5-1650 v3 , this code performs in 0.7s using 6 out of 12 cores . On AMD Ryzen 5 2600 , this

问题 I used to benchmark Linux System Calls with rdtsc to get the counter difference before and after the system call. I interpreted the result as wall clock timer since TSC increments at constant rate and does not stop when entering halt state. The Invariant TSC concept is described as The invariant TSC will run at a constant rate in all ACPI P-, C-. and T-states. Can a constant non-invariant tsc change frequency when changing state from C0 (operating) to C1 (halted)? My current view is that it