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PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms.
Shared memory is declared in the PTX file via lines at the start of the form: .shared .align 8 .b8 pbatch_cache [ 15744 ]; // define 15,744 bytes, aligned to an 8-byte boundary Writing kernels in PTX requires explicitly registering PTX modules via the CUDA Driver API, typically more cumbersome than using the CUDA Runtime API and Nvidia's CUDA ...
CUDA 9.0–9.2 comes with these other components: CUTLASS 1.0 – custom linear algebra algorithms, NVIDIA Video Decoder was deprecated in CUDA 9.2; it is now available in NVIDIA Video Codec SDK; CUDA 10 comes with these other components: nvJPEG – Hybrid (CPU and GPU) JPEG processing; CUDA 11.0–11.8 comes with these other components: [20 ...
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
CUDA support ROCm support [1] Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0: Yes Apache Spark Scala Scala, Python No No Yes Yes Yes Yes Caffe: Berkeley Vision and Learning Center 2013 BSD: Yes Linux, macOS ...
May 2, 2007 1× G80 600 128 1350 — GDDR3 384 1.5 1600 76.8 No 0.3456 No 1.0 170.9 Internal PCIe GPU (full-height, dual-slot) D870 Deskside Computer [d] May 2, 2007 2× G80 600 256 1350 — GDDR3 2× 384 2× 1.5 1600 2× 76.8 No 0.6912 No 1.0 520 Deskside or 3U rack-mount external GPUs S870 GPU Computing Server [d] May 2, 2007 4× G80 600 512 ...
24.7 MTr/mm 2: 25.0 MTr/mm 2: 24.3 MTr/mm 2: 23.2 MTr/mm 2: 23.5 MTr/mm 2: Graphics processing clusters 6 6 3 3 2 Streaming multiprocessors 72 48 36 24 16 CUDA cores: 4608 3072 2304 1536 1024 Texture mapping units: 288 192 144 96 64 Render output units: 96 64 64 48 32 Tensor cores: 576 384 288 — RT cores: 72 48 36 L1 cache: 6.75 MB 4.5 MB 3. ...