The document discusses optimization techniques for deep learning frameworks on Intel CPUs and Fugaku aimed architectures. It introduces oneDNN, a performance library for deep learning operations on Intel CPUs. It discusses issues with C++ implementation, and how just-in-time assembly generation using Xbyak can address these issues by generating optimal code depending on parameters. It also introduces Xbyak_aarch64 for generating optimized code for Fugaku's Scalable Vector Extension instructions.
The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
The document discusses optimization techniques for deep learning frameworks on Intel CPUs and Fugaku aimed architectures. It introduces oneDNN, a performance library for deep learning operations on Intel CPUs. It discusses issues with C++ implementation, and how just-in-time assembly generation using Xbyak can address these issues by generating optimal code depending on parameters. It also introduces Xbyak_aarch64 for generating optimized code for Fugaku's Scalable Vector Extension instructions.
The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
8. ダークネットのいま
ssmjp 8
IPv4 アドレス空間マップ
引用:Dainotti, A., Benson, K., King, A., claffy, k. , Kallitsis, M., Glatz, E., and Dimitropoulos,X.,
“Estimating Internet address space usage through passive measurements”
ライブネット
ダークネット
経路なし
引用:Dainotti, A., Benson, K., King, A., claffy, k. , Kallitsis, M., Glatz, E., and Dimitropoulos,X.,
“Estimating Internet address space usage through passive measurements”
In ACM SIGCOMM Computer Communication Review (CCR) (2014)
2015/2/20
9. ダークネットのいま
ssmjp 9
IPv4 アドレス空間マップ
引用:Dainotti, A., Benson, K., King, A., claffy, k. , Kallitsis, M., Glatz, E., and Dimitropoulos,X.,
“Estimating Internet address space usage through passive measurements”
In ACM SIGCOMM Computer Communication Review (CCR) (2014)
ライブネット
ダークネット
経路なし
全IPv4アドレスの53.7%が
ダークネット or 経路なし
2015/2/20