-NVIDIA CUDA Toolkit provides a development environment for producing high-performance GPU-accelerated applications. With the CUDA Toolkit, you can develop, optimize, plus deploy your applications on GPU-accelerated stuck systems, desktop workstations, enterprise data facilities, cloud-based platforms, and HPC supercomputers. The particular toolkit includes GPU-accelerated libraries, debugging plus optimization tools, a C/C compiler, plus a runtime library to deploy your own application. GPU-accelerated CUDA libraries enable drop-in acceleration across multiple domains such since linear algebra, image and video handling, deep learning, and graph analytics. Intended for developing custom algorithms, you can make use of available integrations with commonly used different languages and numerical packages as well since well-published development APIs. Your CUDA apps can be deployed across all -NVIDIA GPU families available on-premise and GPU instances in the cloud. Using pre-installed capabilities for distributing computations across multi-GPU configurations, scientists and researchers can create applications that scale from single GPU workstations to cloud installations with hundreds of GPUs. IDE with graphical plus command-line tools for debugging, identifying functionality bottlenecks for the GPU and CPU, plus providing context-sensitive optimization guidance. Develop apps using a programming language you currently know, including C, C, Fortran, plus Python. To get started, browse by means of online starting out resources, optimization guides, illustrative examples, and collaborate with the quickly growing developer community. Download NVIDIA CUDA Toolkit for PC today! Features plus Highlights GPU Timestamp: Start timestamp Technique: GPU method name. This really is either "memcpy*" for memory copies or maybe the name associated with a GPU kernel. Memory copies have got a suffix that describes the kind of a memory transfer, e. grams. "memcpyDToHasync" means an asynchronous transfer through Device memory to Host memory GPU Time: It does not take execution time for the particular method on GPU CPU Time: This is the sum of GPU period and CPU overhead to launch that will Method. At driver generated data degree, CPU Time is only CPU over head to launch the Method for non-blocking Methods; for blocking methods it is definitely a sum of GPU time plus CPU overhead. All kernel launches simply by default are non-blocking. But if any kind of profiler counters are enabled kernel starts are blocking. Asynchronous memory copy demands in various streams are non-blocking Stream Identification: Identification number for the stream Articles only for kernel methods Occupancy: Guests is the ratio from the number associated with active warps per multiprocessor towards the optimum number of active warps Profiler desks: Refer the profiler counters section for the list of counters supported grid dimension: Variety of blocks in the grid together the X, Y, and Z proportions are shown as [num_blocks_X num_blocks_Y num_blocks_Z] in one column block dimension: Number of threads in the block together X, Y, and Z dimensions is definitely shown as [num_threads_X num_threads_Y num_threads_Z]] in a single line dyn smem per block: Dynamic distributed memory size per block in bytes sta smem per block: Static distributed memory size per block in bytes reg per thread: Number of signs up per thread Columns only for memcopy methods mem transfer size: Memory exchange size in bytes host mem exchange type: Specifies whether a memory exchange uses "Pageable" or "Page-locked" memory Furthermore Available: Download NVIDIA CUDA Toolkit intended for Mac Download NVIDIA CUDA Toolkit Most recent Version