I think it is reasonable to assume that the CUDA compiler engineers in charge ofptxasare fully aware of the latest developments in the field and that heuristics that consider the general problem constraint by GPU-specific restrictions (such as calling conventions , register aggregation for 64-bit ...
This extra information allows the CUDA compiler to use the read-only data cache and improves performance by more than 2x. Average timings over 10,000 calls, 256,000 threads, CUDA 6.5 RC, NVIDIA Kepler K40. Conclusion It’s important to understand pointer aliasing when writing code where every...
上面说Cache可以更好的利用局部性原理,所谓局部性原理就是优先从留CPU近的存储结构中去寻找当前需要查找的数据,加快数据访问速度从而减少程序中各个变量的存取时间。 关于Cache更多的概念可以参考一下文末的资料1,写得非常好。 “假设 cache line 为 32B。待访问数据大小为 64B,地址在 0x80000001,则需要占用 3 条...
The arguments here are a pointer to the first destination element and the pitch of the destination array, a pointer to the first source element and pitch of the source array, the width and height of the submatrix to transfer, and the memcpy kind. There is also acudaMemcpy3D()function for...
What’s the significance of those crazy frame rates in the context of a competitive first-person shooter? It’s all about smoothness and responsiveness. The easiest way to “feel” the effect of a 300Hz panel is to swap back and forth between its native refresh rate and 60Hz...
randn((experts, hidden_size, intermediate_size), device="cuda", dtype=torch.float16) / 32 gating_output = torch.randn((num_tokens, experts), device="cuda", dtype=torch.float16) # Run once to get peak memory usage start_memory_mb = torch.cuda.max_memory_allocated() // (1024 * ...
Background For indexing operations such as torch.index_select, torch.index_add, and torch.index_reduce, GPU performance is relatively low when handling large input sizes. On an A100 GPU, torch.inde...
self.empty_cache() def empty_cache(self): try: try: gc.collect() # 触发gc的垃圾回收。避免内存一直增长。 if "cuda" in str(self.configs.device): torch.cuda.empty_cache() elif str(self.configs.device) == "mps": @@ -939,4 +940,4 @@ def speed_change(input_audio:np.ndarray, sp...
^如果禁止 use_cache 选项,每次都需要重新计算所在层的 past_key_value。 ^除非把双卡 RTX A6000 定义为消费级显卡 ^https://github.com/fpgaminer/GPTQ-triton ^补充:大概率是第一步计算时产生了大量的零碎 tensor 占用 GPU,第二步计算时 CUDA 的显存管理耗时过长。
cudaHostAllocWriteCombined,写内存可能需要用到缓存,设置这个flag标记写内存不会使用L1,L2 cache,这样做可以让程序通过PCIE总线写内存的时候不会被监视,提升传输速度 cudaHostAllocMapped,将锁页内存映射到设备地址,这块内存有两个地址,一个是cudaHostMalloc返回的主机地址,另一个在设备存储器上,可以通过cudaHostGetDevi...