benchmark_processor(array_gpu, cp.add, 1) # benchmark matrix addition on GPU by using CuPy addition function gpu_time = benchmark_processor(array_gpu, cp.add, 999) # determine how much is GPU faster faster_processor = (gpu_time - cpu_time) / gpu_time * 100 并将结果打印到控制台。
Percent of texels filtered using the "Nereast" sampling method. Average number of scalar fragment shader ALU instructions issued per shaded fragment. Average number of scalar fragment shader EFU instructions issued per shaded fragment. ... 这些都是比较宏观的检测手段,针对某个shader性能的分析,我觉得xc...
Because, one important thing in moving to future hardware models is that they can't afford to suddenly lose all the current benchmarks. So DirectX remains relevant even after the majority of games shipping are using 100 percent software-based rendering techniques, just because those benchmarks c...
GPU直通配置⽂档 https://blogs.vmware.com/apps/2018/09/using-gpus-with-virtual-machines-on-vsphere-part-2- vmdirectpath-i-o.html
In the process of development of GPU, using this method to design each part of the performance indicators, through the simulation shows that the model assessment graphics performance and the measured phase, the error is less than 7.5 percent. ...
Offload all weights to disk by using--percent 0 0 100 0 100 0. This requires very little CPU and GPU memory. Performance Results Generation Throughput (token/s) The corresponding effective batch sizes and lowest offloading devices are in parentheses. Please seeherefor more details. ...
Using device: cuda Tesla K80 Memory Usage: Allocated: 0.3 GB Cached: 0.6 GB As mentioned above, using device it is possible to: To move tensors to the respective device: torch.rand(10).to(device) To create a tensor directly on the device: torch.rand(10, device=device) Which ...
Offload all weights to disk by using--percent 0 0 100 0 100 0. This requires very little CPU and GPU memory. Performance Results Generation Throughput (token/s) The corresponding effective batch sizes and lowest offloading devices are in parentheses. Please seeherefor more details. ...
使用8192个H100-80G GPU和10T Token数据训练175B模型，预估仅需30天即可完成，高效且强大。10T*6*175B/(8192*1000T*50%)/3600/24=30 天 NVIDIA在《Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM》中运用了Activation重计算策略，这一创新要求额外进行一次Forward操作。因此，...
Since Laius is designed for a single GPU, for fair comparison, we schedule two microservices of a benchmark on one GPU using Laius. The total throughput of the benchmark for Laius is calculated as the mini- mum throughputs of all the GPUs. The QoS target of a user query is ranging ...