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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2026, FlagOS Community
# This file is distributed under the same license as the KernelGenBench
# Documentation package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2026.
#
msgid ""
msgstr ""
"Project-Id-Version: KernelGenBench Documentation \n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2026-06-17 13:36+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language: zh_CN\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.17.0\n"
#: ../../source/overview/results.md:1
msgid "Benchmark Results"
msgstr "基准结果"
#: ../../source/overview/results.md:3
msgid "Key findings from KernelGenBench evaluation experiments."
msgstr "KernelGenBench 评估实验的关键发现。"
#: ../../source/overview/results.md:5 ../../source/overview/results.md:9
msgid "Multi-Source Results"
msgstr "多源结果"
#: ../../source/overview/results.md:7
msgid ""
"Evaluation of 210 operators from three sources (ATen, vLLM, cuBLAS) on "
"NVIDIA A100."
msgstr "在 NVIDIA A100 上对来自三个来源（ATen、vLLM、cuBLAS）的 210 个算子进行评估。"
#: ../../source/overview/results.md:9
msgid ""
msgstr ""
#: ../../source/overview/results.md:11 ../../source/overview/results.md:35
msgid "Key Findings"
msgstr "关键发现"
#: ../../source/overview/results.md
msgid "Finding"
msgstr "发现"
#: ../../source/overview/results.md
msgid "Details"
msgstr "详情"
#: ../../source/overview/results.md
msgid "Highest accuracy"
msgstr "最高精度"
#: ../../source/overview/results.md
msgid "Claude Code (Opus-4.6) achieved 87%"
msgstr "Claude Code (Opus-4.6) 达到 87%"
#: ../../source/overview/results.md
msgid "Highest speedup"
msgstr "最高加速比"
#: ../../source/overview/results.md
msgid "AutoKernel (Qwen3.5) achieved 1.02×"
msgstr "AutoKernel (Qwen3.5) 达到 1.02×"
#: ../../source/overview/results.md
msgid "Most challenging"
msgstr "最具挑战性"
#: ../../source/overview/results.md
msgid "cuBLAS operators for all methods"
msgstr "所有方法的 cuBLAS 算子"
#: ../../source/overview/results.md:19
msgid "By Operator Source"
msgstr "按算子来源"
#: ../../source/overview/results.md
msgid "Source"
msgstr "来源"
#: ../../source/overview/results.md
msgid "Best Accuracy"
msgstr "最佳精度"
#: ../../source/overview/results.md
msgid "Best Speedup"
msgstr "最佳加速比"
#: ../../source/overview/results.md
msgid "ATen"
msgstr "ATen"
#: ../../source/overview/results.md
msgid "92% (Claude Code)"
msgstr "92% (Claude Code)"
#: ../../source/overview/results.md
msgid "1.00× (AKO4ALL)"
msgstr "1.00× (AKO4ALL)"
#: ../../source/overview/results.md
msgid "vLLM"
msgstr "vLLM"
#: ../../source/overview/results.md
msgid "68% (Claude Code)"
msgstr "68% (Claude Code)"
#: ../../source/overview/results.md
msgid "1.63× (AutoKernel)"
msgstr "1.63× (AutoKernel)"
#: ../../source/overview/results.md
msgid "cuBLAS"
msgstr "cuBLAS"
#: ../../source/overview/results.md
msgid "94% (Claude Code)"
msgstr "94% (Claude Code)"
#: ../../source/overview/results.md
msgid "0.71× (multiple methods)"
msgstr "0.71× (多种方法)"
#: ../../source/overview/results.md:27 ../../source/overview/results.md:31
msgid "Multi-Chip Results"
msgstr "多芯片结果"
#: ../../source/overview/results.md:29
msgid "Cross-platform evaluation of 110 ATen operators on 6 hardware platforms."
msgstr "在 6 个硬件平台上对 110 个 ATen 算子进行跨平台评估。"
#: ../../source/overview/results.md:31
msgid ""
msgstr ""
#: ../../source/overview/results.md:33
msgid ""
""
msgstr ""
#: ../../source/overview/results.md:33
msgid "Cross-platform Performance"
msgstr "跨平台性能"
#: ../../source/overview/results.md
msgid "Platform variance"
msgstr "平台差异"
#: ../../source/overview/results.md
msgid "Generation performance varies significantly across hardware"
msgstr "生成性能在不同硬件间差异显著"
#: ../../source/overview/results.md
msgid "Cross-platform degradation"
msgstr "跨平台性能下降"
#: ../../source/overview/results.md
msgid "AutoKernel dropped from 87% (NVIDIA) to 25% (Platform E)"
msgstr "AutoKernel 从 87%（NVIDIA）下降到 25%（平台 E）"
#: ../../source/overview/results.md
msgid "Compiler maturity impact"
msgstr "编译器成熟度影响"
#: ../../source/overview/results.md
msgid "Non-NVIDIA platforms require 2× or more tokens and time"
msgstr "非 NVIDIA 平台需要 2 倍或更多的 Token 和时间"
#: ../../source/overview/results.md:43
msgid "Accuracy by Platform"
msgstr "各平台精度"
#: ../../source/overview/results.md
msgid "Platform"
msgstr "平台"
#: ../../source/overview/results.md
msgid "Claude Code"
msgstr "Claude Code"
#: ../../source/overview/results.md
msgid "AKO4ALL"
msgstr "AKO4ALL"
#: ../../source/overview/results.md
msgid "NVIDIA"
msgstr "NVIDIA"
#: ../../source/overview/results.md
msgid "87%"
msgstr "87%"
#: ../../source/overview/results.md
msgid "83%"
msgstr "83%"
#: ../../source/overview/results.md
msgid "Platform A"
msgstr "平台 A"
#: ../../source/overview/results.md
msgid "~70%"
msgstr "~70%"
#: ../../source/overview/results.md
msgid "~60%"
msgstr "~60%"
#: ../../source/overview/results.md
msgid "Platform B"
msgstr "平台 B"
#: ../../source/overview/results.md
msgid "~65%"
msgstr "~65%"
#: ../../source/overview/results.md
msgid "~55%"
msgstr "~55%"
#: ../../source/overview/results.md
msgid "Platform C"
msgstr "平台 C"
#: ../../source/overview/results.md
msgid "~45%"
msgstr "~45%"
#: ../../source/overview/results.md
msgid "Platform D"
msgstr "平台 D"
#: ../../source/overview/results.md
msgid "~35%"
msgstr "~35%"
#: ../../source/overview/results.md
msgid "Platform E"
msgstr "平台 E"
#: ../../source/overview/results.md
msgid "~25%"
msgstr "~25%"
#: ../../source/overview/results.md:54
msgid "Cost Analysis"
msgstr "成本分析"
#: ../../source/overview/results.md
msgid "Method"
msgstr "方法"
#: ../../source/overview/results.md
msgid "Tokens per Success"
msgstr "每次成功所需 Token"
#: ../../source/overview/results.md
msgid "Pass@5"
msgstr "Pass@5"
#: ../../source/overview/results.md
msgid "~50K"
msgstr "~50K"
#: ../../source/overview/results.md
msgid "~500K"
msgstr "~500K"
#: ../../source/overview/results.md
msgid "~5.19M"
msgstr "~5.19M"
#: ../../source/overview/results.md:63
msgid ""
"Large-scale agent evaluations may consume billions of tokens. Plan your "
"budget accordingly."
msgstr "大规模智能体评估可能消耗数十亿 Token。请相应规划您的预算。"