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| <p>Tải công việc: chúng tôi sử dụng Mạng nơ-ron tích chập sâu (deep convolutional |
| neural network), |
| <a href="https://github.com/apache/singa/blob/master/examples/cnn/model/resnet.py">ResNet-50</a> |
| làm ứng dụng. ResNet-50 có 50 lớp tích chập (convolution layers) để phân loại |
| hình ảnh. Nó đòi hỏi 3.8 GFLOPs để đưa vào một hình ảnh (kích thước ảnh 224x224) |
| qua mạng lưới. Kích thước ảnh đầu vào là 224x224.</p> |
| <p>Phần cứng: chúng tôi sử dụng máy p2.8xlarge từ AWS, mỗi máy gồm 8 Nvidia Tesla |
| K80 GPUs, bộ nhớ tổng cộng 96 GB GPU, 32 vCPU, 488 GB main memory, 10 Gbps |
| network bandwidth.</p> |
| <p>Metric: chúng tôi tính thời gian mỗi bước cho mỗi workers để đánh giá khả năng |
| mở rộng của SINGA. Kích thước của mỗi nhóm được cố định ở 32 mỗi GPU. Phương |
| thức training đồng bộ (Synchronous training scheme) được áp dụng. Vì thế, kích |
| thước nhóm hiệu quả là $32N$, trong đó N là số máy GPUs. Chúng tôi so sánh với |
| một hệ thống mở được dùng phổ biến có sử dụng tham số server cấu trúc liên kết. |
| Máy GPU đầu tiên được chọn làm server.</p> |
| <p><img src="/docs/assets/benchmark.png" alt="Thí nghiệm Benchmark"> <br/> <strong>Kiểm tra khả năng mở rộng. |
| Bars được dùng cho thông lượng (throughput); lines dùng cho lượng kết nối |
| (communication cost).</strong></p> |
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