NVIDIA CUDA Toolkit addresses a small group of developers and programmers working in C and C++ that are on the lookout for the official CUDA development environment from NVIDIA. With NVIDIA CUDA Toolkit, you can freely build GPU-accelerated application software projects. First things first, CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the puissance of the graphics processing unit (GPU). CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared recollection. The on-chip shared recollection sanctions parallel tasks running on these cores to apportion data without sending it over the system recollection bus. With NVIDIA CUDA Toolkit, the arrival of multicore CPUs and manycore GPUs designates that mainstream processor chips are now parallel systems. Furthermore, their parallelism perpetuates to scale with Moore’s law. The challenge is to develop application software that transparently scales its parallelism to leverage the incrementing number of processor cores, much as 3D graphics applications transparently scale their parallelism to manycore GPUs with widely varying numbers of cores. The CUDA parallel programming model is designed to surmount this challenge while maintaining a low learning curve for programmers habituated with standard programming languages such as C. At its core are three key abstractions – a hierarchy of thread groups, shared recollections, and barrier synchronization – that are simply exposed to the programmer as a minimal set of language extensions. Overall, the NVIDIA CUDA Toolkit is the one and only environment to start your projects in if you want to take full advantage of this wonderful platform. Just make sure you have an NVIDIA graphics adapter that support CUDA technology before launching into this innovating climate of unlimited possibilities..
NVIDIA CUDA technology is the world’s only C language environment that enables developers and programmers to write software to solve complex computational problems in a fraction of the time by tapping into the many-core parallel processing power of GPUs. With millions of CUDA-capable GPUs already deployed, thousands of software programmers are already using the free CUDA software tools to accelerate applications-from video and audio encoding to oil and gas exploration, product design, medical imaging, and scientific research. NVIDIA CUDA-enabled GPUs power millions of desktops, notebooks, workstations, and supercomputers around the world, accelerating computationally-intensive tasks for consumers, professionals, scientists, and researchers.
Here are some key features of “NVIDIA CUDA Toolkit”:
- GPU Timestamp: Start time stamp
- Method: GPU method name. This is either “memcpy*” for memory copies or the name of a GPU kernel. Memory copies have a suffix that describes the type of a memory transfer, e.g. “memcpyDToHasync” means an asynchronous transfer from Device memory to Host memory
- GPU Time: It is the execution time for the method on GPU
- CPU Time:It is sum of GPU time and CPU overhead to launch that Method. At driver generated data level, CPU Time is only CPU overhead to launch the Method for non-blocking Methods; for blocking methods it is sum of GPU time and CPU overhead. All kernel launches by default are non-blocking. But if any profiler counters are enabled kernel launches are blocking. Asynchronous memory copy requests in different streams are non-blocking
- Stream Id : Identification number for the stream
- Columns only for kernel methods
- Occupancy : Occupancy is the ratio of the number of active warps per multiprocessor to the maximum number of active warps
- Profiler counters: Refer the profiler counters section for list of counters supported
- grid size : Number of blocks in the grid along the X, Y and Z dimensions is shown as [num_blocks_X num_blocks_Y num_blocks_Z] in a single column
- block size : Number of threads in a block along X, Y and Z dimensions is shown as [num_threads_X num_threads_Y num_threads_Z]] in a single column
- dyn smem per block: Dynamic shared memory size per block in bytes
- sta smem per block: Static shared memory size per block in bytes
- reg per thread: Number of registers per thread
- Columns only for memcopy methods
- mem transfer size: Memory transfer size in bytes
- host mem transfer type: Specifies whether a memory transfer uses “Pageable” or “Page-locked” memory
- General CUDA:
- MPS (Multi-Process Service) is a runtime service designed to let multiple MPI (Message Passing Interface) processes using CUDA run concurrently on a single GPU in a way that’s transparent to the MPI program. A CUDA program runs in MPS mode if the MPS control daemon is running on the system. When a CUDA program starts, it connects to the MPS control daemon (if possible), which then creates an MPS server for the connecting client if one does not already exist for the user (UID) that launched the client.
- With the CUDA 5.5 Toolkit, there are some restrictions that are now enforced that may cause existing projects that were building on CUDA 5.0 to fail. For projects that use -Xlinker with nvcc, you need to ensure the arguments after -Xlinker are quoted. In CUDA 5.0, -Xlinker -rpath /usr/local/cuda/lib would succeed; in CUDA 5.5 -Xlinker “-rpath /usr/local/cuda/lib” is now necessary.
- The Toolkit is using a new installer on Windows. The installer is able to install any selecti…
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