1665349440

# VSL.jl: Julia Bindings to The intel Vector Statistics Library

## VSL.jl

This package provides bindings to the Intel Vector Statistics Library.

## Using VSL.jl

You must have the Intel® Math Kernel Library installed to use VSL.jl, and the shared library must be in a directory known to the linker.

VML.jl provides several basic random number generators (BRNGs) and distributions, and each distribution has at least one method to generate random number. After VSL.jl loaded, you can use the distributions such like the followings:

``````julia> using VSL

julia> brng = BasicRandomNumberGenerator(VSL_BRNG_MT19937, 12345);
# A BRNG created, in which 12345 is the random seed.

julia> u = Uniform(brng, 0.0, 1.0); # Create a uniform distribution between 0.0 and 1.0.

julia> rand(u) # Generate one random number.
0.41661986871622503

julia> rand(u, 2, 3) # Generate an random 2*3 array.
2×3 Array{Float64,2}:
0.732685   0.820175  0.802848
0.0101692  0.825207  0.29864

julia> A = Array{Float64}(3, 4);

julia> rand!(u, A) # Fill an array with random numbers.
3×4 Array{Float64,2}:
0.855138  0.193661  0.436228  0.124267
0.368412  0.270245  0.161688  0.874174
0.931785  0.566008  0.373064  0.432936
``````

### Basic random number generators

Use the Enum `BRNGType` to set the type of BRNG.

### Supported distributions

Contigurous: `Uniform`, `Gaussian`, `GaussianMV`, `Exponential`, `Laplace`, `Weibull`, `Cauchy`, `Rayleigh`, `Lognormal`, `Gumbel`, `Gamma`, `Beta`

Discrete: `UniformDiscrete`, `UniformBits`, `UniformBits32`, `UniformBits64`, `Bernoulli`, `Geometric`, `Binomial`, `Hypergeometric`, `Poisson`, `PoissonV`, `NegBinomial`

### Notes

Most of the discrete distributions return values of 32-bit integer. Please be careful when using those distributions.

Author: Sunoru
Source Code: https://github.com/sunoru/VSL.jl

1665349440

## VSL.jl

This package provides bindings to the Intel Vector Statistics Library.

## Using VSL.jl

You must have the Intel® Math Kernel Library installed to use VSL.jl, and the shared library must be in a directory known to the linker.

VML.jl provides several basic random number generators (BRNGs) and distributions, and each distribution has at least one method to generate random number. After VSL.jl loaded, you can use the distributions such like the followings:

``````julia> using VSL

julia> brng = BasicRandomNumberGenerator(VSL_BRNG_MT19937, 12345);
# A BRNG created, in which 12345 is the random seed.

julia> u = Uniform(brng, 0.0, 1.0); # Create a uniform distribution between 0.0 and 1.0.

julia> rand(u) # Generate one random number.
0.41661986871622503

julia> rand(u, 2, 3) # Generate an random 2*3 array.
2×3 Array{Float64,2}:
0.732685   0.820175  0.802848
0.0101692  0.825207  0.29864

julia> A = Array{Float64}(3, 4);

julia> rand!(u, A) # Fill an array with random numbers.
3×4 Array{Float64,2}:
0.855138  0.193661  0.436228  0.124267
0.368412  0.270245  0.161688  0.874174
0.931785  0.566008  0.373064  0.432936
``````

### Basic random number generators

Use the Enum `BRNGType` to set the type of BRNG.

### Supported distributions

Contigurous: `Uniform`, `Gaussian`, `GaussianMV`, `Exponential`, `Laplace`, `Weibull`, `Cauchy`, `Rayleigh`, `Lognormal`, `Gumbel`, `Gamma`, `Beta`

Discrete: `UniformDiscrete`, `UniformBits`, `UniformBits32`, `UniformBits64`, `Bernoulli`, `Geometric`, `Binomial`, `Hypergeometric`, `Poisson`, `PoissonV`, `NegBinomial`

### Notes

Most of the discrete distributions return values of 32-bit integer. Please be careful when using those distributions.

Author: Sunoru
Source Code: https://github.com/sunoru/VSL.jl

1665345480

## IntelVectorMath.jl (formerly VML.jl)

This package provides bindings to the Intel MKL Vector Mathematics Functions. This is often substantially faster than broadcasting Julia's built-in functions, especially when applying a transcendental function over a large array. Until Julia 0.6 the package was registered as `VML.jl`.

Similar packages are Yeppp.jl, which wraps the open source Yeppp library, and AppleAccelerate.jl, which provides access to macOS's Accelerate framework.

### Warning for macOS

There is currently the following issue between the `CompilerSupportLibraries_jll` artifact, which is used for example by `SpecialFunctions.jl`, and `MKL_jll`. Unless `MKL_jll` is loaded first, there might be wrong results coming from a small number of function for particular input array lengths. If you are unsure which, if any, your used packages might load this artifact, loading `IntelVectorMath` as the very first package should be fine.

## Basic install

To install IntelVectorMath.jl run

``````julia> ] add IntelVectorMath
``````

Since version 0.4 `IntelVectorMath` uses the `MKL_jll` artifact, which is shared with other packages uses MKL, removing several other dependencies. This has the side effect that from version 0.4 onwards this package requires at least Julia 1.3.

For older versions of Julia `IntelVectorMath v0.3` downloads its own version of MKL and keeps only the required files in its own directory. As such installing MKL.jl or MKL via intel are no longer required, and may mean some duplicate files if they are present. However, this package will adopt the new artifact system in the next minor version update and fix this issue. In the event that MKL was not installed properly you will get an error when first `using` it. Please try running

``````julia> ] build IntelVectorMath
``````

If this does not work, please open an issue and include the output of `<packagedir>/deps/build.log`.

#### Renaming from VML

If you used this package prior to its renaming, you may have to run `] rm VML` first. Otherwise there will be a conflict due to the UUID.

## Using IntelVectorMath

After loading `IntelVectorMath`, you have the supported function listed below, for example `IntelVectorMath.sin(rand(100))`. These should provide a significant speed-up over broadcasting the Base functions. Since the package name is quite long, an alias `IVM` is also exported to allow `IVM.sin(rand(100))` after `using` the package. If you `import` the package, you can add this alias via `const IVM = IntelVectorMath`. Equally, you can replace `IVM` with another alias of your choice.

#### Example

``````julia> using IntelVectorMath, BenchmarkTools

julia> a = randn(10^4);

julia> @btime sin.(\$a);     # apply Base.sin to each element
102.128 μs (2 allocations: 78.20 KiB)

julia> @btime IVM.sin(\$a);  # apply IVM.sin to the whole array
20.900 μs (2 allocations: 78.20 KiB)

julia> b = similar(a);

julia> @btime IVM.sin!(b, a);  # in-place version
20.008 μs (0 allocations: 0 bytes)
``````

### Accuracy

By default, IntelVectorMath uses `VML_HA` mode, which corresponds to an accuracy of <1 ulp, matching the accuracy of Julia's built-in openlibm implementation, although the exact results may be different. To specify low accuracy, use `vml_set_accuracy(VML_LA)`. To specify enhanced performance, use `vml_set_accuracy(VML_EP)`. More documentation regarding these options is available on Intel's website.

## Performance

Summary of Results:

Relative speed of IntelVectorMath/Base: The height of the bars is how fast IntelVectorMath is compared to using broadcasting for functions in Base

Full Results:

Real Functions - Full Benchmark Results

Complex Functions - Full Benchmark Results

Real Functions - Performance over dimensions

Tests were performed on an Intel(R) Core(TM) i5-8250U @ 1.6 [GHz] 1800 Mhz. The dashed line indicates equivalent performance for IntelVectorMath versus the implementations in Base.

## Supported functions

IntelVectorMath.jl supports the following functions, most for Float32 and Float64, while some also take complex numbers.

### Unary functions

Allocating forms have signature `f(A)`. Mutating forms have signatures `f!(A)` (in place) and `f!(out, A)` (out of place). The last 9 functions have been moved from Base to `SpecialFunctions.jl` or have no Base equivalent.

### Binary functions

Allocating forms have signature `f(A, B)`. Mutating forms have signature `f!(out, A, B)`.

## Next steps

Next steps for this package

•  Windows support
•  Basic Testing
•  Travis and AppVeyor testing
•  Move Testing to GitHub Actions
•  Add test for using standalone MKL
•  Update Benchmarks
•  Add tests for mutating functions
•  Add own dependency management via BinaryProvider
•  Update function list in README
•  Adopt Julia 1.3 artifact system, breaking backwards compatibility

IntelVectorMath.jl uses CpuId.jl to detect if your processor supports the newer `avx2` instructions, and if not defaults to `libmkl_vml_avx`. If your system does not have AVX this package will currently not work for you. If the CPU feature detection does not work for you, please open an issue.

Author: JuliaMath
Source Code: https://github.com/JuliaMath/IntelVectorMath.jl

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## Critical Intel Flaw Afflicts Several Motherboards, Server Systems, Compute Modules

Intel is warning of a rare critical-severity vulnerability affecting several of its motherboards, server systems and compute modules. The flaw could allow an unauthenticated, remote attacker to achieve escalated privileges.

The recently patched flaw (CVE-2020-8708) ranks 9.6 out of 10 on the CVSS scale, making it critical. Dmytro Oleksiuk, who discovered the flaw, told Threatpost that it exists in the firmware of Emulex Pilot 3. This baseboard-management controller is a service processor that monitors the physical state of a computer, network server or other hardware devices via specialized sensors.

Click to register!

Emulex Pilot 3 is used by various motherboards, which aggregate all the server components into one system. Also impacted are various server operating systems, and some Intel compute modules, which are electronic circuits, packaged onto a circuit board, that provide various functions.

The critical flaw stems from improper-authentication mechanisms in these Intel products before version 1.59.

In bypassing authentication, an attacker would be able to access to the KVM console of the server. The KVM console can access the system consoles of network devices to monitor and control their functionality. The KVM console is like a remote desktop implemented in the baseboard management controller – it provides an access point to the display, keyboard and mouse of the remote server, Oleksiuk told Threatpost.

The flaw is dangerous as it’s remotely exploitable, and attackers don’t need to be authenticated to exploit it – though they need to be located in the same network segment as the vulnerable server, Oleksiuk told Threatpost.

“The exploit is quite simple and very reliable because it’s a design flaw,” Oleksiuk told Threatpost.

Beyond this critical flaw, Intel also fixed bugs tied to 22 critical-, high-, medium- and low-severity CVEs affecting its server board, systems and compute modules. Other high-severity flaws include a heap-based overflow (CVE-2020-8730) that’s exploitable as an authenticated user; incorrect execution-assigned permissions in the file system (CVE-2020-8731); and a buffer overflow in daemon (CVE-2020-8707) — all three of which enable escalated privileges.

Click to enlarge.

Oleksiuk was credited with reporting CVE-2020-8708, as well as CVE-2020-8706, CVE-2020-8707. All other CVEs were found internally by Intel.

Affected server systems include: The R1000WT and R2000WT families, R1000SP, LSVRP and LR1304SP families and R1000WF and R2000WF families.

Impacted motherboards include: The S2600WT family, S2600CW family, S2600KP family, S2600TP family, S1200SP family, S2600WF family, S2600ST family and S2600BP family.

Finally, impacted compute modules include: The HNS2600KP family, HNS2600TP family and HNS2600BP family. More information regarding patches is available in Intel’s security advisory.

Intel also issued an array of other security advisories addressing high-severity flaws across its product lines, including ones that affect Intel Graphics Drivers, Intel’s RAID web console 3 for Windows, Intel Server Board M10JNP2SB and Intel NUCs.

#vulnerabilities #compute module #critical flaw #cve-2020-8708 #intel #intel critical flaw #intel flaw #intel motherboard #intel server board #patch #privilege escalation #security vulnerability #server system

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## GraphViz.jl

This package provides an interface to the the `GraphViz` package for graph visualization. There are two primary entry points:

• The `GraphViz.load` function (not exported) to load graphs from a file
• The `dot"""` string macro for literal inline specifications of graphs

Both of these accept `Graph` type accepts graph in DOT format. To load a graph from a non-constant string, use `GraphViz.load` with an `IOBuffer`.

Getting started

If you already have a graph you would like to work with, the following code snippets may be helpful. If not, have a look at the "Simple Examples" section below

``````using GraphViz
dot"""
digraph graphname {
a -> b -> c;
b -> d;
}
""")
``````

Usage

After obtaining the package through the package manager, the following suffices to load the package:

``````using GraphViz
``````

Note that graphviz has many configuration options. In particular, both the Cairo and the GTK backends may be disabled by default.

Simple Examples

Try the following in an IJulia Notebook (this example is taken from here):

``````dot"""
graph graphname {
// The label attribute can be used to change the label of a node
a [label="Foo"];
// Here, the node shape is changed.
b [shape=box];
// These edges both have different line properties
a -- b -- c [color=blue];
b -- d [style=dotted];
}
"""``````

Author: JuliaGraphs
Source Code: https://github.com/JuliaGraphs/GraphViz.jl

1661053320

## Bootstrap.jl: Statistical Bootstrapping

### Motivation

Bootstrapping is a widely applicable technique for statistical estimation.

## Functionality

Bootstrapping statistics with different resampling methods:

• Random resampling with replacement (`BasicSampling`)
• Antithetic resampling, introducing negative correlation between samples (`AntitheticSampling`)
• Balanced random resampling, reducing bias (`BalancedSampling`)
• Exact resampling, iterating through all unique resamples (`ExactSampling`): deterministic bootstrap, suited for small samples sizes
• Resampling of residuals in generalized linear models (`ResidualSampling`, `WildSampling`)
• Maximum Entropy bootstrapping for dependent and non-stationary datasets (`MaximumEntropySampling`)

Confidence intervals:

• Basic (`BasicConfInt`)
• Percentile (`PercentileConfInt`)
• Normal distribution (`NormalConfInt`)
• Studendized (`StudentConfInt`)
• Bias-corrected and accelerated (BCa) (`BCaConfInt`)

## Installation

The `Bootstrap` package is part of the Julia ecosphere and the latest release version can be installed with

``````using Pkg
``````

More details on packages and how to manage them can be found in the package section of the Julia documentation.

## Examples

This example illustrates the basic usage and cornerstone functions of the package. More elaborate cases are covered in the documentation notebooks.

``````  using Bootstrap
``````

Our observations in `some_data` are sampled from a standard normal distribution.

``````  some_data = randn(100);
``````

Let's bootstrap the standard deviation (`std`) of our data, based on 1000 resamples and with different bootstrapping approaches.

``````  using Statistics  # the `std` methods live here

n_boot = 1000

## basic bootstrap
bs1 = bootstrap(std, some_data, BasicSampling(n_boot))

## balanced bootstrap
bs2 = bootstrap(std, some_data, BalancedSampling(n_boot))
``````

We can explore the properties of the bootstrapped samples, for example, the estimated bias and standard error of our statistic.

``````  bias(bs1)
stderror(bs1)
``````

Furthermore, we can estimate confidence intervals (CIs) for our statistic of interest, based on the bootstrapped samples.

``````  ## calculate 95% confidence intervals
cil = 0.95;

## basic CI
bci1 = confint(bs1, BasicConfInt(cil));

## percentile CI
bci2 = confint(bs1, PercentileConfInt(cil));

## BCa CI
bci3 = confint(bs1, BCaConfInt(cil));

## Normal CI
bci4 = confint(bs1, NormalConfInt(cil));
``````

## References

The bootstrapping wikipedia article is a comprehensive introduction into the topic. An extensive description of the bootstrap is the focus of the book Davison and Hinkley (1997): Bootstrap Methods and Their Application. Most of the methodology covered in the book is implemented in the boot package for the R programming language. More references are listed in the documentation for further reading.

## Contributions and Feedback

Contributions of any kind are very welcome. Please feel free to open pull requests or issues if you have suggestions for changes, ideas or questions.

Does it have anything to do with twitter themes, webpage frameworks, compiling, ...?

No, not really. This package focuses on an interesting area in statistics, but the term bootstrapping is also used in other contexts. You can check wikipedia for a longer list of meanings associated with bootstrapping.

## Package Status

The package uses semantic versioning.