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Jan 20, 2021
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More stuff #6

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966e7e5
More stuff
DilumAluthge Jan 20, 2021
0e74e1d
Update plotting.jl
DilumAluthge Jan 20, 2021
02e8fad
Update plotting.jl
DilumAluthge Jan 20, 2021
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5 changes: 4 additions & 1 deletion .github/workflows/ci.yml
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Expand Up @@ -70,7 +70,10 @@ jobs:
using Documenter: doctest
using BLASBenchmarks
doctest(BLASBenchmarks)'
env:
JULIA_NUM_THREADS: 2
- run: julia --project=docs docs/make.jl
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
JULIA_NUM_THREADS: 2
1 change: 1 addition & 0 deletions docs/make.jl
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Expand Up @@ -14,6 +14,7 @@ makedocs(;
pages=[
"Home" => "index.md",
"Usage" => "usage.md",
"Memory Required for Large Matrices" => "memory-required.md",
"Public API" => "public-api.md",
"Internals (Private)" => "internals.md",
],
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3 changes: 0 additions & 3 deletions docs/src/assets/gemm_Float32_10_30_haswell_AVX2__multithreaded_logscale.svg
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3 changes: 0 additions & 3 deletions docs/src/assets/gemm_Float64_10_30_haswell_AVX2__multithreaded_logscale.svg
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8 changes: 0 additions & 8 deletions docs/src/index.md
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Expand Up @@ -8,11 +8,3 @@ BLASBenchmarks is a Julia package for benchmarking BLAS libraries.

The source code for this package is available in the
[GitHub repository](https://github.com/chriselrod/BLASBenchmarks.jl).

## Float64

![Float64](../assets/gemm_Float64_10_30_haswell_AVX2__multithreaded_logscale.svg)

## Float32

![Float32](../assets/gemm_Float32_10_30_haswell_AVX2__multithreaded_logscale.svg)
161 changes: 161 additions & 0 deletions docs/src/memory-required.md
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@@ -0,0 +1,161 @@
```@meta
CurrentModule = BLASBenchmarks
```

# Memory Required for Large Matrices

These tables show how much memory is required for four matrices of the given size and type. (We need four matrices: `A`, `B`, `C1`, and `C2`.)

## Float64

| Matrix Size | Memory |
| ----------- | ------ |
| 1k by 1k | 0.03 GiB |
| 2k by 2k | 0.12 GiB |
| 3k by 3k | 0.27 GiB |
| 4k by 4k | 0.48 GiB |
| 5k by 5k | 0.75 GiB |
| 6k by 6k | 1.07 GiB |
| 7k by 7k | 1.46 GiB |
| 8k by 8k | 1.91 GiB |
| 9k by 9k | 2.41 GiB |
| 10k by 10k | 2.98 GiB |
| 11k by 11k | 3.61 GiB |
| 12k by 12k | 4.29 GiB |
| 13k by 13k | 5.04 GiB |
| 14k by 14k | 5.84 GiB |
| 15k by 15k | 6.71 GiB |
| 16k by 16k | 7.63 GiB |
| 17k by 17k | 8.61 GiB |
| 18k by 18k | 9.66 GiB |
| 19k by 19k | 10.76 GiB |
| 20k by 20k | 11.92 GiB |
| 30k by 30k | 26.82 GiB |
| 40k by 40k | 47.68 GiB |
| 50k by 50k | 74.51 GiB |
| 60k by 60k | 107.29 GiB |
| 70k by 70k | 146.03 GiB |
| 80k by 80k | 190.73 GiB |
| 90k by 90k | 241.4 GiB |
| 100k by 100k | 298.02 GiB |

## Float32

| Matrix Size | Memory |
| ----------- | ------ |
| 1k by 1k | 0.01 GiB |
| 2k by 2k | 0.06 GiB |
| 3k by 3k | 0.13 GiB |
| 4k by 4k | 0.24 GiB |
| 5k by 5k | 0.37 GiB |
| 6k by 6k | 0.54 GiB |
| 7k by 7k | 0.73 GiB |
| 8k by 8k | 0.95 GiB |
| 9k by 9k | 1.21 GiB |
| 10k by 10k | 1.49 GiB |
| 11k by 11k | 1.8 GiB |
| 12k by 12k | 2.15 GiB |
| 13k by 13k | 2.52 GiB |
| 14k by 14k | 2.92 GiB |
| 15k by 15k | 3.35 GiB |
| 16k by 16k | 3.81 GiB |
| 17k by 17k | 4.31 GiB |
| 18k by 18k | 4.83 GiB |
| 19k by 19k | 5.38 GiB |
| 20k by 20k | 5.96 GiB |
| 30k by 30k | 13.41 GiB |
| 40k by 40k | 23.84 GiB |
| 50k by 50k | 37.25 GiB |
| 60k by 60k | 53.64 GiB |
| 70k by 70k | 73.02 GiB |
| 80k by 80k | 95.37 GiB |
| 90k by 90k | 120.7 GiB |
| 100k by 100k | 149.01 GiB |

## Int64

| Matrix Size | Memory |
| ----------- | ------ |
| 1k by 1k | 0.03 GiB |
| 2k by 2k | 0.12 GiB |
| 3k by 3k | 0.27 GiB |
| 4k by 4k | 0.48 GiB |
| 5k by 5k | 0.75 GiB |
| 6k by 6k | 1.07 GiB |
| 7k by 7k | 1.46 GiB |
| 8k by 8k | 1.91 GiB |
| 9k by 9k | 2.41 GiB |
| 10k by 10k | 2.98 GiB |
| 11k by 11k | 3.61 GiB |
| 12k by 12k | 4.29 GiB |
| 13k by 13k | 5.04 GiB |
| 14k by 14k | 5.84 GiB |
| 15k by 15k | 6.71 GiB |
| 16k by 16k | 7.63 GiB |
| 17k by 17k | 8.61 GiB |
| 18k by 18k | 9.66 GiB |
| 19k by 19k | 10.76 GiB |
| 20k by 20k | 11.92 GiB |
| 30k by 30k | 26.82 GiB |
| 40k by 40k | 47.68 GiB |
| 50k by 50k | 74.51 GiB |
| 60k by 60k | 107.29 GiB |
| 70k by 70k | 146.03 GiB |
| 80k by 80k | 190.73 GiB |
| 90k by 90k | 241.4 GiB |
| 100k by 100k | 298.02 GiB |

## Int32

| Matrix Size | Memory |
| ----------- | ------ |
| 1k by 1k | 0.01 GiB |
| 2k by 2k | 0.06 GiB |
| 3k by 3k | 0.13 GiB |
| 4k by 4k | 0.24 GiB |
| 5k by 5k | 0.37 GiB |
| 6k by 6k | 0.54 GiB |
| 7k by 7k | 0.73 GiB |
| 8k by 8k | 0.95 GiB |
| 9k by 9k | 1.21 GiB |
| 10k by 10k | 1.49 GiB |
| 11k by 11k | 1.8 GiB |
| 12k by 12k | 2.15 GiB |
| 13k by 13k | 2.52 GiB |
| 14k by 14k | 2.92 GiB |
| 15k by 15k | 3.35 GiB |
| 16k by 16k | 3.81 GiB |
| 17k by 17k | 4.31 GiB |
| 18k by 18k | 4.83 GiB |
| 19k by 19k | 5.38 GiB |
| 20k by 20k | 5.96 GiB |
| 30k by 30k | 13.41 GiB |
| 40k by 40k | 23.84 GiB |
| 50k by 50k | 37.25 GiB |
| 60k by 60k | 53.64 GiB |
| 70k by 70k | 73.02 GiB |
| 80k by 80k | 95.37 GiB |
| 90k by 90k | 120.7 GiB |
| 100k by 100k | 149.01 GiB |

## Generating These Tables

```julia
mem_req(s, T) = 4s^2*sizeof(T) / (1 << 30)

function print_table(::Type{T}, Ns = nothing) where {T}
println("| Matrix Size | Memory |")
println("| ----------- | ------ |")
if Ns isa Nothing
_Ns = sort(unique(vcat(collect(1:1:20), collect(20:10:100))))
else
_Ns = Ns
end
for N in _Ns
mem = mem_req(N * 1_000, T)
m = round(mem; digits = 2)
println("| $(N)k by $(N)k | $(m) GiB |")
end
return nothing
end
```
42 changes: 30 additions & 12 deletions src/plotting.jl
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Expand Up @@ -26,13 +26,33 @@ function default_plot_directory()
return joinpath(_pkgdir(), "docs", "src", "assets")
end

"""
default_plot_filename(br::BenchmarkResult;
desc,
logscale)
"""
function default_plot_filename(br::BenchmarkResult{T};
desc::AbstractString,
logscale::Bool) where {T}
df = br.df
l, u = extrema(df.Size)
if logscale
desc *= "_logscale"
end
desc = (br.threaded ? "_multithreaded" : "_singlethreaded") * desc
suffix = pick_suffix(desc)
return "gemm_$(string(T))_$(l)_$(u)_$(suffix)"
end

"""
plot(br::BenchmarkResult;
desc = "",
logscale = true,
width = 1200,
height = 600,
plot_directory = default_plot_directory())
plot_directory = default_plot_directory(),
plot_filename = default_plot_filename(br; desc = desc, logscale = logscale),
file_extensions = ["svg", "png"])
"""
function plot(br::BenchmarkResult{T}; kwargs...) where {T}
_plot(br; kwargs...)
Expand All @@ -47,6 +67,8 @@ function _plot(
width::Real = 1200,
height::Real = 600,
plot_directory::AbstractString = default_plot_directory(),
plot_filename::AbstractString = default_plot_filename(br; desc = desc, logscale = logscale),
file_extensions = ["svg", "png"],
) where {T}
df = br.df
plt = if logscale
Expand All @@ -62,16 +84,12 @@ function _plot(
width = width, height = height
)
end
l, u = extrema(df.Size)
if logscale
desc *= "_logscale"
end
desc = (br.threaded ? "_multithreaded" : "_singlethreaded") * desc
suffix = pick_suffix(desc)
mkpath(plot_directory)
foo1 =
foo2 =
save(joinpath(plot_directory, "gemm_$(string(T))_$(l)_$(u)_$(suffix).svg"), plt)
save(joinpath(plot_directory, "gemm_$(string(T))_$(l)_$(u)_$(suffix).png"), plt)
return foo1, foo2
_filenames = String[]
for ext in file_extensions
_filename = joinpath(plot_directory, "$(plot_filename).$(ext)")
save(_filename, plt)
push!(_filenames, _filename)
end
return _filenames
end
40 changes: 39 additions & 1 deletion src/runbenchmark.jl
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Expand Up @@ -102,10 +102,48 @@ Base.axes(ls::LogSpace) = axes(ls.r)
Base.eltype(::LogSpace) = Int

"""
all_libs()
"""
function all_libs()
libs = Symbol[
:BLIS,
:Gaius,
:MKL,
:Octavian,
:OpenBLAS,
:Tullio,
]
return libs
end

function _integer_libs()
libs_to_exclude = Symbol[:BLIS, :MKL, :OpenBLAS]
return sort(unique(setdiff(all_libs(), libs_to_exclude)))
end

"""
default_libs(T)
"""
function default_libs(::Type{T}) where {T}
if T <: Integer
return _integer_libs()
else
return all_libs()
end
end

"""
runbench(T = Float64;
libs = default_libs(T),
sizes = logspace(2, 4000, 200),
threaded::Bool = Threads.nthreads() > 1,
A_transform = identity,
B_transform = identity,
sleep_time = 0.0)
"""
function runbench(
::Type{T} = Float64;
libs = [:BLIS, :Gaius, :MKL, :Octavian, :OpenBLAS, :Tullio],
libs = default_libs(T),
sizes = logspace(2, 4000, 200),
threaded::Bool = Threads.nthreads() > 1,
A_transform = identity,
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