NVIDIA CUDA Toolkit for Mac

NVIDIA CUDA Toolkit for Mac provides a development environment for creating high-performance GPU-accelerated applications.

With the CUDA Toolkit for macOS, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms, and HPC supercomputers.



The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to deploy your application.

GPU-accelerated CUDA libraries enable drop-in acceleration across multiple domains such as linear algebra, image and video processing, deep learning, and graph analytics. For developing custom algorithms, you can use available integrations with commonly used languages and numerical packages as well as well-published development APIs.

Your CUDA applications can be deployed across all NVIDIA GPU families available on-premise and on GPU instances in the cloud. Using built-in capabilities for distributing computations across multi-GPU configurations, scientists and researchers can develop applications that scale from single GPU workstations to cloud installations with thousands of GPUs.

IDE with graphical and command-line tools for debugging, identifying performance bottlenecks on the GPU and CPU, and providing context-sensitive optimization guidance. Develop applications using a programming language you already know, including C, C++, Fortran, and Python.

To get started, browse through online getting started resources, optimization guides, illustrative examples, and collaborate with the rapidly growing developer community. Download NVIDIA CUDA Toolkit for macOS today!

The macOS host tools provided are:

Nsight Systems - a system profiler and timeline trace tool supporting Pascal and newer GPUs

Nsight Compute - a CUDA kernel profiler supporting Volta and new GPUs

NOTE: that the MacOS host version these tools, deprecated in previous releases, have been dropped as of CUDA Toolkit 12.5.
Follow these links to review the supported operating systems for these tools.

Visual Profiler - a CUDA kernel and system profiler and timeline trace tool supporting older GPUs

cuda-gdb - a GPU and CPU CUDA application debugger

Features and Highlights

• GPU Timestamp: Start timestamp
• 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 the 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 the 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 a 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

PROS

• Massive Parallel Processing Power
• Optimized for NVIDIA GPUs
• Strong Developer Support
• Wide AI & HPC Applications
• Seamless Integration with Libraries

CONS

• Limited to NVIDIA GPUs
• Steep Learning Curve
• High Power Consumption
• Hardware Upgrade Costs
• Not Ideal for All Workloads

Also Available: Download NVIDIA CUDA Toolkit for Windows