NVIDIA TITAN V 不專業開箱 - 3C
By Gary
at 2018-03-18T20:46
at 2018-03-18T20:46
Table of Contents
一大早被郵差門鈴吵醒,趕緊開門收貨開箱,拿出一幾乎全白的盒子放在桌上:
https://i.imgur.com/eXLDkXA.jpg
打開後,除了 TITAN V 本體,還有兩本手冊與一條 DP 轉 DVI 的轉接線:
https://i.imgur.com/6vKbiLq.jpg
背板有 3 個 DisplayPort 加 1 個 HDMI 接頭:
https://i.imgur.com/U9K85dq.jpg
將本體拿出靜電袋,準備裝進系統:
https://i.imgur.com/ECRr1Ut.jpg
現有一張 ASUS ROG STRIX-GTX1080TI-O11G-GAMING:
https://i.imgur.com/nXXIVmM.jpg
插好插滿:
https://i.imgur.com/EX7rItr.jpg
開機測試:
https://i.imgur.com/ldY5EDD.jpg
利馬跑個之前寫的tensorflow程式,用小量資料組訓練個一期,結果跟預想差不多,
在不為 GV100 寫客製最佳化程式下其實只比 1080Ti 快 30% 左右,要完整用到那
640 個 Tensor Core 的運算能力,還有很多文件要看程式要寫:
https://i.imgur.com/YGDy8Oi.png
不過如果你的程式有用到雙精度浮點運算(FP64),倒是不用改程式就可以直接獲得
10 倍左右的加速,TITAN V 是 NVIDIA 首次在消費型顯卡下放全速 FP64 計算能力:
https://i.imgur.com/lIgIfMm.png
NVIDIA 的專業卡才會有的 ECC 記憶體,在 TITAN V 上還是沒有開放:
https://i.imgur.com/JjbUC5k.png
最後附上一個簡單的規格比較表:
╒═══════╤═══════╤══════╤══════╤═══════╕
│ │ Titan V │ Titan Xp │GTX 1080 Ti │ V100 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ CUDA Cores │ 5120 │ 3840 │ 3584 │ 5120 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ FP64 Cores │ 2560 │ 120 │ 112 │ 2560 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ Boost Clock │ 1455 MHz │ 1582 MHz │ 1582 MHz │ 1370 MHz │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 記憶體大小 │ 12GB │ 12GB │ 11GB │ 16GB │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 記憶體介面 │3072-bit HBM2 │384-bit G5X │352-bit G5X │4096-bit HBM2 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 記憶體頻寬 │ 653 GB/s │ 547 GB/s │ 484 GB/s │ 900 GB/s │
╞═══════╪═══════╪══════╪══════╪═══════╡
│半精度(Tensor)│ 110 TFLOPS │0.19 TFLOPS │0.17 TFLOPS │ 112 TFLOPS │
╞═══════╪═══════╪══════╪══════╪═══════╡
│單精度 (FP32) │ 13.8 TFLOPS │12.1 TFLOPS │11.3 TFLOPS │ 14 TFLOPS │
╞═══════╪═══════╪══════╪══════╪═══════╡
│雙精度 (FP64) │ 6.9 TFLOPS │0.38 TFLOPS │0.35 TFLOPS │ 7 TFLOPS │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ GPU 晶片 │ GV100 │ GP102 │ GP102 │ GV100 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ Die Size │ 815 mm2 │ 471 mm2 │ 471 mm2 │ 815 mm2 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 電晶體數量 │ 21.1B │ 12B │ 12B │ 21.1B │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 製程技術 │TSMC 12nm FFN │TSMC 16nm FF│TSMC 16nm FF│TSMC 12nm FFN │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 售價 │ 10萬 │ 4萬 │ 2.8萬 │ 30萬 │
╘═══════╧═══════╧══════╧══════╧═══════╛
--
https://i.imgur.com/eXLDkXA.jpg
打開後,除了 TITAN V 本體,還有兩本手冊與一條 DP 轉 DVI 的轉接線:
https://i.imgur.com/6vKbiLq.jpg
背板有 3 個 DisplayPort 加 1 個 HDMI 接頭:
https://i.imgur.com/U9K85dq.jpg
將本體拿出靜電袋,準備裝進系統:
https://i.imgur.com/ECRr1Ut.jpg
現有一張 ASUS ROG STRIX-GTX1080TI-O11G-GAMING:
https://i.imgur.com/nXXIVmM.jpg
插好插滿:
https://i.imgur.com/EX7rItr.jpg
開機測試:
https://i.imgur.com/ldY5EDD.jpg
利馬跑個之前寫的tensorflow程式,用小量資料組訓練個一期,結果跟預想差不多,
在不為 GV100 寫客製最佳化程式下其實只比 1080Ti 快 30% 左右,要完整用到那
640 個 Tensor Core 的運算能力,還有很多文件要看程式要寫:
https://i.imgur.com/YGDy8Oi.png
不過如果你的程式有用到雙精度浮點運算(FP64),倒是不用改程式就可以直接獲得
10 倍左右的加速,TITAN V 是 NVIDIA 首次在消費型顯卡下放全速 FP64 計算能力:
https://i.imgur.com/lIgIfMm.png
NVIDIA 的專業卡才會有的 ECC 記憶體,在 TITAN V 上還是沒有開放:
https://i.imgur.com/JjbUC5k.png
最後附上一個簡單的規格比較表:
╒═══════╤═══════╤══════╤══════╤═══════╕
│ │ Titan V │ Titan Xp │GTX 1080 Ti │ V100 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ CUDA Cores │ 5120 │ 3840 │ 3584 │ 5120 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ FP64 Cores │ 2560 │ 120 │ 112 │ 2560 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ Boost Clock │ 1455 MHz │ 1582 MHz │ 1582 MHz │ 1370 MHz │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 記憶體大小 │ 12GB │ 12GB │ 11GB │ 16GB │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 記憶體介面 │3072-bit HBM2 │384-bit G5X │352-bit G5X │4096-bit HBM2 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 記憶體頻寬 │ 653 GB/s │ 547 GB/s │ 484 GB/s │ 900 GB/s │
╞═══════╪═══════╪══════╪══════╪═══════╡
│半精度(Tensor)│ 110 TFLOPS │0.19 TFLOPS │0.17 TFLOPS │ 112 TFLOPS │
╞═══════╪═══════╪══════╪══════╪═══════╡
│單精度 (FP32) │ 13.8 TFLOPS │12.1 TFLOPS │11.3 TFLOPS │ 14 TFLOPS │
╞═══════╪═══════╪══════╪══════╪═══════╡
│雙精度 (FP64) │ 6.9 TFLOPS │0.38 TFLOPS │0.35 TFLOPS │ 7 TFLOPS │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ GPU 晶片 │ GV100 │ GP102 │ GP102 │ GV100 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ Die Size │ 815 mm2 │ 471 mm2 │ 471 mm2 │ 815 mm2 │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 電晶體數量 │ 21.1B │ 12B │ 12B │ 21.1B │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 製程技術 │TSMC 12nm FFN │TSMC 16nm FF│TSMC 16nm FF│TSMC 12nm FFN │
╞═══════╪═══════╪══════╪══════╪═══════╡
│ 售價 │ 10萬 │ 4萬 │ 2.8萬 │ 30萬 │
╘═══════╧═══════╧══════╧══════╧═══════╛
--
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