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allocbench.jl.ipynb
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allocbench.jl.ipynb
{
"cells": [
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.913Z",
"end_time": "2024-01-15T19:15:03.498000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "versioninfo()",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "Julia Version 1.10.0\nCommit 3120989f39b (2023-12-25 18:01 UTC)\nBuild Info:\n Official https://julialang.org/ release\nPlatform Info:\n OS: Linux (x86_64-linux-gnu)\n CPU: 12 × Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz\n WORD_SIZE: 64\n LIBM: libopenlibm\n LLVM: libLLVM-15.0.7 (ORCJIT, skylake)\n Threads: 2 on 12 virtual cores\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "ref: [go で巨大 slice を作ると遅い #Go - Qiita](https://qiita.com/Nabetani/items/2856fa1ce0057ba0ab4a) \noriginal: [nabetani/allocbench](https://github.com/nabetani/allocbench)"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.914Z",
"end_time": "2024-01-15T19:15:03.808000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "]activate .",
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": "\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `/mnt/polonium_data/ipynb_root/toybox/malloc`\n",
"name": "stderr"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.915Z",
"end_time": "2024-01-15T19:15:13.020000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "]add Plots",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": "\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `/mnt/polonium_data/ipynb_root/toybox/malloc/Project.toml`\n\u001b[32m\u001b[1m No Changes\u001b[22m\u001b[39m to `/mnt/polonium_data/ipynb_root/toybox/malloc/Manifest.toml`\n",
"name": "stderr"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.915Z",
"end_time": "2024-01-15T19:15:13.104000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "]st",
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": "\u001b[32m\u001b[1mStatus\u001b[22m\u001b[39m `/mnt/polonium_data/ipynb_root/toybox/malloc/Project.toml`\n \u001b[90m[91a5bcdd] \u001b[39mPlots v1.39.0\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.916Z",
"end_time": "2024-01-15T19:15:13.436000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "function ptr2UInt(ptr::Ptr)\n GC.@preserve ptr reinterpret(UInt, ptr)\nend\nfunction ptr2UInt(arr::AbstractArray)\n ptr2UInt(Base.pointer_from_objref(Ref(arr)))\nend",
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 5,
"data": {
"text/plain": "ptr2UInt (generic function with 2 methods)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.916Z",
"end_time": "2024-01-15T19:15:13.552000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "ptr2UInt([1, 2, 3])",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 6,
"data": {
"text/plain": "0x00007f76e3b67fe0"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.917Z",
"end_time": "2024-01-15T19:15:13.569000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "function test_v(i)\n # おそらく C++ 版の test_m 相当\n d = @timed Vector{UInt8}(undef, 1<<i)\n v = d.value\n t = d.time - d.gctime # 計測時間からGCにかかった時間を引くことで算出\n # println(\"v, $i, $t, $(Base.pointer_from_objref(Ref(v)))\")\n (;name=\"v\", i, t, p=ptr2UInt(v))\nend",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 7,
"data": {
"text/plain": "test_v (generic function with 1 method)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.918Z",
"end_time": "2024-01-15T19:15:14.084000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "test_v(1)",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 8,
"data": {
"text/plain": "(name = \"v\", i = 1, t = 1.473e-6, p = 0x00007f772f655110)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.918Z",
"end_time": "2024-01-15T19:15:16.518000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "for i in 1:29\n (;name, i, t, p) = test_v(i)\n println(\"$name, $i, $t, 0x$(string(p, base=16, pad=16))\")\n GC.gc()\nend",
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": "v, 1, 1.194e-6, 0x00007f7731119d80\nv, 2, 1.215e-6, 0x00007f772ee0c010\nv, 3, 1.07e-6, 0x00007f772ee0c010\nv, 4, 1.604e-6, 0x00007f772ee0c010\nv, 5, 1.529e-6, 0x00007f772ee0c010\nv, 6, 1.162e-6, 0x00007f772ee0c010\nv, 7, 1.702e-6, 0x00007f772ee0c010\nv, 8, 9.73e-7, 0x00007f772ee0c010\nv, 9, 1.097e-6, 0x00007f772ee0c010\nv, 10, 1.042e-6, 0x00007f772ee0c010\nv, 11, 2.2967e-5, 0x00007f772ee0c010\nv, 12, 0.000161822, 0x00007f772ee0c010\nv, 13, 7.7049e-5, 0x00007f772ee0c010\nv, 14, 5.371e-6, 0x00007f772ee0c010\nv, 15, 7.353e-6, 0x00007f772ee0c010\nv, 16, 4.742e-6, 0x00007f772ee0c010\nv, 17, 1.1128e-5, 0x00007f772ee0c010\nv, 18, 9.142e-6, 0x00007f772ee0c010\nv, 19, 1.1797e-5, 0x00007f772ee0c010\nv, 20, 1.4904e-5, 0x00007f772ee0c010\nv, 21, 1.6233e-5, 0x00007f772ee0c010\nv, 22, 1.5134e-5, 0x00007f772ee0c010\nv, 23, 1.7053e-5, 0x00007f772ee0c010\nv, 24, 1.3239e-5, 0x00007f772ee0c010\nv, 25, 1.654e-5, 0x00007f772ee0c010\nv, 26, 2.3171999999999988e-5, 0x00007f76cc0ac010\nv, 27, 2.0773e-5, 0x00007f772ee0c010\nv, 28, 2.6955000000000002e-5, 0x00007f76cc0ac010\nv, 29, 2.3180000000000005e-5, 0x00007f772ee0c010\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.918Z",
"end_time": "2024-01-15T19:15:16.530000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "function test_z(i)\n # ゼロクリア版\n d = @timed zeros(UInt8, 1<<i)\n v = d.value\n t = d.time - d.gctime # 計測時間からGCにかかった時間を引くことで算出\n # println(\"z, $i, $t, $(Base.pointer_from_objref(Ref(v)))\")\n (;name=\"z\", i, t, p=ptr2UInt(v))\nend",
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 10,
"data": {
"text/plain": "test_z (generic function with 1 method)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.919Z",
"end_time": "2024-01-15T19:15:16.539000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "test_z(1)",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 11,
"data": {
"text/plain": "(name = \"z\", i = 1, t = 1.453e-6, p = 0x00007f772eedc670)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.919Z",
"end_time": "2024-01-15T19:15:19.286000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "for i in 1:29\n (;name, i, t, p) = test_z(i)\n println(\"$name, $i, $t, 0x$(string(p, base=16, pad=16))\")\n GC.gc()\nend",
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": "z, 1, 1.104e-6, 0x00007f772f9ce540\nz, 2, 1.483e-6, 0x00007f772ee0c010\nz, 3, 1.034e-6, 0x00007f772ee0c010\nz, 4, 1.216e-6, 0x00007f772ee0c010\nz, 5, 1.557e-6, 0x00007f772ee0c010\nz, 6, 1.106e-6, 0x00007f772ee0c010\nz, 7, 9.91e-7, 0x00007f772ee0c010\nz, 8, 1.223e-6, 0x00007f772ee0c010\nz, 9, 1.178e-6, 0x00007f772ee0c010\nz, 10, 1.208e-6, 0x00007f772ee0c010\nz, 11, 2.058e-5, 0x00007f772ee0c010\nz, 12, 5.848e-6, 0x00007f772ee0c010\nz, 13, 1.2541e-5, 0x00007f772ee0c010\nz, 14, 1.1451e-5, 0x00007f772ee0c010\nz, 15, 2.1289e-5, 0x00007f772ee0c010\nz, 16, 3.0821e-5, 0x00007f772ee0c010\nz, 17, 5.1804e-5, 0x00007f772ee0c010\nz, 18, 9.007e-5, 0x00007f772ee0c010\nz, 19, 0.000177383, 0x00007f772ee0c010\nz, 20, 0.00034267, 0x00007f772ee0c010\nz, 21, 0.00037769, 0x00007f772ee0c010\nz, 22, 0.001321823, 0x00007f772ee0c010\nz, 23, 0.001465578, 0x00007f772ee0c010\nz, 24, 0.005411964, 0x00007f772ee0c010\nz, 25, 0.010689259, 0x00007f772ee0c010\nz, 26, 0.02127242, 0x00007f76cc0ac010\nz, 27, 0.041470613999999996, 0x00007f772ee0c010\nz, 28, 0.081935343, 0x00007f76cc0ac010\nz, 29, 0.16208625499999998, 0x00007f772ee0c010\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.919Z",
"end_time": "2024-01-15T19:15:19.296000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "function test_r(i)\n # C++ の reserve 版相当？\n d = @timed begin\n v = Vector{UInt8}()\n sizehint!(v, 1<<i)\n end\n v = d.value\n t = d.time - d.gctime # 計測時間からGCにかかった時間を引くことで算出\n # println(\"r, $i, $t, $(Base.pointer_from_objref(Ref(v)))\")\n (;name=\"r\", i, t, p=ptr2UInt(v))\nend",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 13,
"data": {
"text/plain": "test_r (generic function with 1 method)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.920Z",
"end_time": "2024-01-15T19:15:19.304000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "test_r(1)",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 14,
"data": {
"text/plain": "(name = \"r\", i = 1, t = 1.71e-6, p = 0x00007f772eedc9b0)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.920Z",
"end_time": "2024-01-15T19:15:22.060000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "for i in 1:29\n (;name, i, t, p) = test_r(i)\n println(\"$name, $i, $t, 0x$(string(p, base=16, pad=16))\")\n GC.gc()\nend",
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": "r, 1, 1.651e-6, 0x00007f772efaf840\nr, 2, 1.558e-6, 0x00007f772ee0c010\nr, 3, 2.006e-6, 0x00007f772ee0c010\nr, 4, 1.664e-6, 0x00007f772ee0c010\nr, 5, 1.628e-6, 0x00007f772ee0c010\nr, 6, 1.996e-6, 0x00007f772ee0c010\nr, 7, 2.292e-6, 0x00007f772ee0c010\nr, 8, 1.729e-6, 0x00007f772ee0c010\nr, 9, 1.725e-6, 0x00007f772ee0c010\nr, 10, 1.409e-6, 0x00007f772ee0c010\nr, 11, 1.7601e-5, 0x00007f772ee0c010\nr, 12, 1.3265e-5, 0x00007f772ee0c010\nr, 13, 1.3747e-5, 0x00007f772ee0c010\nr, 14, 1.4831e-5, 0x00007f772ee0c010\nr, 15, 2.5949e-5, 0x00007f772ee0c010\nr, 16, 3.05e-5, 0x00007f772ee0c010\nr, 17, 5.4538e-5, 0x00007f772ee0c010\nr, 18, 9.7615e-5, 0x00007f772ee0c010\nr, 19, 0.000184471, 0x00007f772ee0c010\nr, 20, 0.000339778, 0x00007f772ee0c010\nr, 21, 0.00038457, 0x00007f772ee0c010\nr, 22, 0.0013408, 0x00007f772ee0c010\nr, 23, 0.001443224, 0x00007f772ee0c010\nr, 24, 0.005421139, 0x00007f772ee0c010\nr, 25, 0.010415936, 0x00007f772ee0c010\nr, 26, 0.020904425, 0x00007f76cc0ac010\nr, 27, 0.041266837, 0x00007f772ee0c010\nr, 28, 0.082065273, 0x00007f76cc0ac010\nr, 29, 0.163026333, 0x00007f772ee0c010\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.920Z",
"end_time": "2024-01-15T19:15:22.069000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "function test_s(i)\n # C++ の resize 版相当\n d = @timed begin\n v = Vector{UInt8}()\n resize!(v, 1<<i)\n end\n v = d.value\n t = d.time - d.gctime # 計測時間からGCにかかった時間を引くことで算出\n # println(\"s, $i, $t, $(Base.pointer_from_objref(Ref(v)))\")\n (;name=\"s\", i, t, p=ptr2UInt(v))\nend",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 16,
"data": {
"text/plain": "test_s (generic function with 1 method)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.921Z",
"end_time": "2024-01-15T19:15:22.078000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "test_s(1)",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 17,
"data": {
"text/plain": "(name = \"s\", i = 1, t = 1.629e-6, p = 0x00007f772eedd140)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.921Z",
"end_time": "2024-01-15T19:15:24.806000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "for i in 1:29\n (;name, i, t, p) = test_s(i)\n println(\"$name, $i, $t, 0x$(string(p, base=16, pad=16))\")\n GC.gc()\nend",
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": "s, 1, 1.503e-6, 0x00007f772f9ce050\ns, 2, 1.874e-6, 0x00007f772ee0c010\ns, 3, 1.379e-6, 0x00007f772ee0c010\ns, 4, 1.654e-6, 0x00007f772ee0c010\ns, 5, 1.642e-6, 0x00007f772ee0c010\ns, 6, 2.151e-6, 0x00007f772ee0c010\ns, 7, 1.537e-6, 0x00007f772ee0c010\ns, 8, 1.833e-6, 0x00007f772ee0c010\ns, 9, 1.593e-6, 0x00007f772ee0c010\ns, 10, 2.049e-6, 0x00007f772ee0c010\ns, 11, 2.0162e-5, 0x00007f772ee0c010\ns, 12, 5.546e-6, 0x00007f772ee0c010\ns, 13, 7.431e-6, 0x00007f772ee0c010\ns, 14, 5.634e-6, 0x00007f772ee0c010\ns, 15, 9.128e-6, 0x00007f772ee0c010\ns, 16, 7.608e-6, 0x00007f772ee0c010\ns, 17, 1.1529e-5, 0x00007f772ee0c010\ns, 18, 1.2886e-5, 0x00007f772ee0c010\ns, 19, 2.0769e-5, 0x00007f772ee0c010\ns, 20, 3.5567e-5, 0x00007f772ee0c010\ns, 21, 6.5898e-5, 0x00007f772ee0c010\ns, 22, 0.000123047, 0x00007f772ee0c010\ns, 23, 0.000240438, 0x00007f772ee0c010\ns, 24, 0.000487974, 0x00007f772ee0c010\ns, 25, 0.010579059, 0x00007f772ee0c010\ns, 26, 0.020713799, 0x00007f76cc0ac010\ns, 27, 0.041054096, 0x00007f772ee0c010\ns, 28, 0.081772427, 0x00007f76cc0ac010\ns, 29, 0.161747696, 0x00007f772ee0c010\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Plot"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.922Z",
"end_time": "2024-01-15T19:15:25.914000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "using Plots",
"execution_count": 19,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.922Z",
"end_time": "2024-01-15T19:15:26.169000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "gr()",
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 20,
"data": {
"text/plain": "Plots.GRBackend()"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.922Z",
"end_time": "2024-01-15T19:15:26.246000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "plt = plot(yscale=:log, leg=:topleft);",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.923Z",
"end_time": "2024-01-15T19:15:30.141000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "res = Vector{Float64}(undef, 29);\nfor i=1:29\n res[i] = test_v(i).t\n GC.gc()\nend\nplt = plot!(plt, 1:29, res, label=\"Vector{UInt8}(undef, N)\", line=(5, :solid));",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.924Z",
"end_time": "2024-01-15T19:15:34.284000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "res = Vector{Float64}(undef, 29);\nfor i=1:29\n res[i] = test_z(i).t\n GC.gc()\nend\nplt = plot!(plt, 1:29, res, label=\"zeros(UInt8, N)\", line=(5, :dash));",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.924Z",
"end_time": "2024-01-15T19:15:42.576000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "res = Vector{Float64}(undef, 29);\nfor i=1:29\n res[i] = test_r(i).t\n GC.gc()\nend\nplt = plot!(plt, 1:29, res, label=\"sizehint!(Vector{UInt8}(), N)\", line=(5, :dashdot));",
"execution_count": 24,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.924Z",
"end_time": "2024-01-15T19:15:42.576000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "res = Vector{Float64}(undef, 29);\nfor i=1:29\n res[i] = test_s(i).t\n GC.gc()\nend\nplt = plot!(plt, 1:29, res, label=\"resize!(Vector{UInt8}(), N)\", line=(5, :dashdotdot));",
"execution_count": 25,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.925Z",
"end_time": "2024-01-15T19:15:43.369000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "plt",
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 26,
"data": {
"image/png": 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},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-01-15T10:15:01.925Z",
"end_time": "2024-01-15T19:15:43.725000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "savefig(plt, \"allocbench_jl.png\")",
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 27,
"data": {
"text/plain": "\"/mnt/polonium_data/ipynb_root/toybox/malloc/allocbench_jl.png\""
},
"metadata": {}
}
]
}
],
"metadata": {
"kernelspec": {
"name": "julia-1.10",
"display_name": "Julia 1.10.0",
"language": "julia"
},
"language_info": {
"file_extension": ".jl",
"name": "julia",
"mimetype": "application/julia",
"version": "1.10.0"
},
"gist": {
"id": "",
"data": {
"description": "allocbench.jl.ipynb",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Raw
Project.toml
[deps]
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
[compat]
julia = "1.6"
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