View SIMDjl_performance.jl
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julia> function vector_dot(B, C) | |
a = zero(eltype(B)) | |
for i in eachindex(B,C) | |
@inbounds a += B[i] * C[i] | |
end | |
return a | |
end | |
vector_dot (generic function with 1 method) | |
julia> function vector_dot_macro(B, C) |
View readqcd.jl
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using DelimitedFiles | |
using LinearAlgebra | |
using Test | |
function readqcd(fname::AbstractString; verify=true) | |
open(fname, "r") do f | |
header_str = readuntil(f, "END_HEADER\n") | |
header_mat = readdlm(IOBuffer(header_str), '=') | |
header = Dict(strip(header_mat[i, 1]) => header_mat[i, 2] for i in 2:size(header_mat, 1)) |
View nvlink_bench.jl
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using CUDA | |
using BenchmarkTools | |
using Statistics | |
using UnicodePlots | |
# bandwidth test | |
abstract type BytesSIPrefixed end | |
struct B <: BytesSIPrefixed | |
value::Int64 | |
end |
View Manifest.toml
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# This file is machine-generated - editing it directly is not advised | |
julia_version = "1.7.1" | |
manifest_format = "2.0" | |
[[deps.ArgTools]] | |
uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" | |
[[deps.Artifacts]] | |
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" |
View gist:909003a87bcdd2aef228591728394972
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"id": "202a91a0-8ac9-4d47-a628-85786980cbb2", | |
"metadata": {}, | |
"source": [ | |
"# CPU\n", | |
"\n", | |
"## Single-threaded sparse mat-vec product" |
View gist:33a5b57de78cce6a3cecef88d4e2c5e7
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"id": "202a91a0-8ac9-4d47-a628-85786980cbb2", | |
"metadata": {}, | |
"source": [ | |
"# CPU\n", | |
"\n", | |
"## Single-threaded sparse mat-vec product" |
View bench.jl
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print("Loading modules...") | |
using BenchmarkTools | |
using CUDA | |
using DataFrames | |
using CSV | |
using Printf | |
println("done!") | |
const a = 3.1415f0 |
View gpu_saxpy_comparison.jl
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# ============================================================================== | |
# Example program for CUDA Julia tutorial | |
# Written by: Carsten Bauer | |
# ============================================================================== | |
using BenchmarkTools | |
using CUDA | |
using Printf | |
function saxpy_gpu_kernel!(z,a,x,y) | |
i = (blockIdx().x - 1) * blockDim().x + threadIdx().x |
View gist:fd6cc660a4aad707ba087919a2d0eed0
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(itensors) pkg> add ITensors | |
Updating registry at `/p/project/chku27/hku273/.julia/registries/General` | |
┌ Error: curl_easy_setopt: 48 | |
└ @ Downloads.Curl /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Downloads/src/Curl/utils.jl:36 | |
┌ Error: curl_easy_setopt: 48 | |
└ @ Downloads.Curl /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Downloads/src/Curl/utils.jl:36 | |
┌ Error: curl_easy_setopt: 48 | |
└ @ Downloads.Curl /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.6/Downloads/src/Curl/utils.jl:36 | |
Resolving package versions... | |
┌ Error: curl_easy_setopt: 48 |
View meshgrid.jl
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""" | |
meshgrid(xvec) = meshgrid(xvec, xvec) | |
Produces a 2D meshgrid `X,X` by repeating xvec in y-dimension and xvec in x-dimension. | |
""" | |
meshgrid(v::AbstractVector{T}) where T<:Number = meshgrid(v, v) | |
""" | |
meshgrid(xvec, yvec) | |
Produces a 2D meshgrid `X,Y` by repeating xvec in y-dimension and yvec in x-dimension. | |
""" |