4/7/2023 0 Comments Nvidia ram optimizer![]() ![]() For compute intensive workloads (high compute to data transfer ratio) the optimized version is only 10% slower than the ideal scenario. The optimized approach is 1.3x-1.5x faster than the original multi-stream code. Figure 5 shows timings in ms for the naive and optimized methods and two additional lines: no overlap using a single stream (sum of kernel and prefetch times), and ideal overlap (maximum of kernel and prefetch times). The overall speedup from better overlapping will depend on your compute to copy ratio. I ran the benchmark by using 16 tiles of 256MB and varying the compute workload weight to see the performance impact. NVIDIA Visual Profiler Timeline demonstrating good three-way overlap between device-to-host/host-to-device prefetches and CUDA kernels for the optimized case. offload current tile to the cpu after the kernel is completed using the deferred pathĬudaMemPrefetchAsync(a + tile_size * i, tile_size * sizeof(size_t), cudaCpuDeviceId, s1) įigure 4 shows the profiler timeline for this new code with almost perfect three-way overlap (compute, DtoH and HtoD). make sure the stream is idle to force non-deferred HtoD prefetches firstĬudaMemPrefetchAsync(a + tile_size * (i+1), tile_size * sizeof(size_t), 0, s2) ![]() prefetch next tile to the gpu in a separate stream Size_t warp_total = (size + STRIDE_64K-1) / STRIDE_64K ĬudaMemPrefetchAsync(a + tile_size * (i-1), tile_size * sizeof(size_t), cudaCpuDeviceId, s1) įor (int j = 0 j >(tile_size, a + tile_size * i) Int warps_per_grid = (blockDim.x * gridDim.x) > 5 Size_t tid = threadIdx.x + blockIdx.x * blockDim.x _global_ void stream_thread(data_type *ptr, const size_t size, The following simple CUDA kernel reads or writes a chunk of memory in a contiguous fashion. If Unified Memory performance is good on this common access pattern, we can remove all manual data transfers and just directly access the pointers relying on automatic migration. Although this type of access pattern is quite basic, it is fundamental for many applications. I will focus on a streaming example that reads or writes a contiguous range of data originally resident in the system memory. In this post I’ll break it down step by step and show you what you can do to optimize your code to get the most out of Unified Memory. To get the best Unified Memory performance it’s important to understand how on-demand page migration works. Unified Memory combines the advantages of explicit copies and zero-copy access: the GPU can access any page of the entire system memory and at the same time migrate the data on-demand to its own memory for high bandwidth access. Zero-copy access provides fine-grained direct access to the entire system memory, but the speed is limited by the interconnect (PCIe or NVLink) and it’s not possible to take advantage of data locality. While this usually gives the best performance, it requires very careful management of GPU resources and predictable access patterns. Traditionally, developers have used explicit memory copies to transfer data. ![]() Many real-world codes have to selectively use data on the GPU due to its limited memory capacity, and it is the programmer’s responsibility to move only necessary parts of the working set to GPU memory. This is especially important for applications that iterate over the same data multiple times or have a high flops/byte ratio. Making the most of GPU performance requires the data to be as close to the GPU as possible. While GPU architectures have very fast HBM or GDDR memory, they have limited capacity. ![]() Many of today’s applications process large volumes of data. ![]()
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