10 Ways CUDA 6.5 Improves Performance and Productivity

Today we’re excited to announce the release of the CUDA Toolkit version 6.5. CUDA 6.5 adds a number of features and improvements to the CUDA platform, including support for CUDA Fortran in developer tools, user-defined callback functions in cuFFT, new occupancy calculator APIs, and more.


Last year we introduced CUDA on ARM, and in March we released the Jetson TK1 developer board, which enables development of CUDA on the NVIDIA Tegra K1 system-on-a-chip which includes a quad-core 32-bit ARM CPU and an NVIDIA Kepler GPU. There is a lot of excitement about developing mobile and embedded parallel computing applications on Jetson TK1. And this week at the Hot Chips conference, we provided more details about our upcoming 64-bit Denver ARM CPU architecture.

CUDA 6.5 takes the next step, enabling CUDA on 64-bit ARM platforms. The heritage of ARM64 is in low-power, scale-out data centers and microservers, while GPUs are built for ultra-fast compute performance. When we combine the two, we have a compelling solution for HPC. ARM64 provides power efficiency, system configurability, and a large, open ecosystem. GPUs bring to the table high-throughput, power-efficient compute performance, a large HPC ecosystem, and hundreds of CUDA-accelerated applications. For HPC applications, ARM64 CPUs can offload the heavy lifting of computational tasks to GPUs. CUDA and GPUs make ARM64 competitive in HPC from day one.

Development platforms available now for CUDA on ARM64 include the Cirrascale RM1905D HPC Development Platform and the E4 ARKA EK003Eurotech has announced a system available later this year. These platforms are built on Applied Micro X-Gene 8-core 2.4GHz ARM64 CPUs, Tesla K20 GPU Accelerators, and CUDA 6.5. As Figure 1 shows, performance of CUDA-accelerated applications on ARM64+GPU systems is competitive with x86+GPU systems.

Figure 1: CUDA-Accelerated applications provide high performance on ARM64+GPU systems.

Continue reading


CUDA Pro Tip: Occupancy API Simplifies Launch Configuration

CUDA programmers often need to decide on a block size to use for a kernel launch. For key kernels, its important to understand the constraints of the kernel and the GPU it is running on to choose a block size that will result in good performance. One common heuristic used to choose a good block size is to aim for high occupancy, which is the ratio of the number of active warps per multiprocessor to the maximum number of warps that can be active on the multiprocessor at once. Higher occupancy does not always mean higher performance, but it is a useful metric for gauging the latency hiding ability of a kernel.

Release Candidate Available
Become a CUDA Registered Developer and download now!

Before CUDA 6.5, calculating occupancy was tricky. It required implementing a complex computation that took account of the present GPU and its capabilities (including register file and shared memory size), and the properties of the kernel (shared memory usage, registers per thread, threads per block). Implementating the occupancy calculation is difficult, so very few programmers take this approach, instead using the occupancy calculator spreadsheet included with the CUDA Toolkit to find good block sizes for each supported GPU architecture.

CUDA 6.5 includes several new runtime functions to aid in occupancy calculations and launch configuration. The core occupancy calculator API, cudaOccupancyMaxActiveBlocksPerMultiprocessor produces an occupancy prediction based on the block size and shared memory usage of a kernel. This function reports occupancy in terms of the number of concurrent thread blocks per multiprocessor. Note that this value can be converted to other metrics. Multiplying by the number of warps per block yields the number of concurrent warps per multiprocessor; further dividing concurrent warps by max warps per multiprocessor gives the occupancy as a percentage.

Continue reading


CUDA Pro Tip: Profiling MPI Applications

When I profile MPI+CUDA applications, sometimes performance issues only occur for certain MPI ranks. To fix these, it’s necessary to identify the MPI rank where the performance issue occurs. Before CUDA 6.5 it was hard to do this because the CUDA profiler only shows the PID of the processes and leaves the developer to figure out the mapping from PIDs to MPI ranks. Although the mapping can be done manually, for example for OpenMPI via the command-line option --display-map, it’s tedious and error prone. A solution which solves this for the command-line output of nvprof is described here . In this post I will describe how the new output file naming of nvprof to be introduced with CUDA 6.5 can be used to conveniently analyze the performance of a MPI+CUDA application with nvprof and the NVIDIA Visual Profiler (nvvp).

Profiling MPI applications with nvprof and nvvp

Collecting data with nvprof

nvprof supports dumping the profile to a file which can be later imported into nvvp. To generate a profile for a MPI+CUDA application I simply start nvprof with the MPI launcher and up to CUDA 6 I used the string “%p” in the output file name. nvprof automatically replaces that string with the PID and generates a separate file for each MPI rank. With CUDA 6.5, the string “%q{ENV}” can be used to name the output file of nvprof. This allows us to include the MPI rank in the output file name by utilizing environment variables automatically set by the MPI launcher (mpirun or mpiexec). E.g. for OpenMPI OMPI_COMM_WORLD_RANK is set to the MPI rank for each launched process.

$ mpirun -np 2 nvprof -o simpleMPI.%q{OMPI_COMM_WORLD_RANK}.nvprof ./simpleMPI
Running on 2 nodes
==18811== NVPROF is profiling process 18811, command: ./simpleMPI
==18813== NVPROF is profiling process 18813, command: ./simpleMPI
Average of square roots is: 0.667279
==18813== Generated result file: simpleMPI.1.nvprof
==18811== Generated result file: simpleMPI.0.nvprof

Continue reading