One of ArrayFire’s biggest features is the ability for code to be written just once and run on a plethora of devices. In this post, we show the outputs of af::info() from various devices available to us.
Desktop Processors
AMD GPU/CPU (OpenCL)
ArrayFire v2.1 (OpenCL, 64-bit Linux, build 4b9115c) License: Standalone (/home/pavan/.arrayfire.lic) Addons: MGL4, DLA, SLA Platform: AMD Accelerated Parallel Processing, Driver: 1526.3 (VM) [0]: Tahiti, 2864 MB, OpenCL Version: 1.2 1 : AMD FX(tm)-8350 Eight-Core Processor, 7953 MB, OpenCL Version: 1.2 Compute Device: [0]
AMD APU (OpenCL)
ArrayFire v2.1 (OpenCL, 64-bit Linux, build 586ef59) License: Standalone (/home/arrayfire/.arrayfire.lic) Addons: MGL4, DLA, SLA Platform: AMD Accelerated Parallel Processing, Driver: 1445.5 (VM) [0]: Spectre, 624 MB, OpenCL Version: 1.2 1 : AMD A10-7850K APU with Radeon(TM) R7 Graphics, 6885 MB, OpenCL Version: 1.2 Compute Device: [0]
Intel CPU (OpenCL)
ArrayFire v2.1 (OpenCL, 64-bit Linux, build 4b9115c) License: Standalone (/home/arrayfire/.arrayfire.lic) Addons: MGL4, DLA, SLA Platform: Intel(R) OpenCL, Driver: 1.2.0.83073 [0]: Intel(R) Core(TM) i7-4770K CPU @ 3.50GHz, 7922 MB, OpenCL Version: 1.2 Compute Device: [0]
Intel HD Graphics (OpenCL)
ArrayFire v2.1 (OpenCL, 64-bit Mac OSX, build a01d9b3) License: Standalone (/Users/arrayfire/arrayfire.lic) Addons: MGL4, DLA, SLA Platform: Apple, Driver: 1.2(Jun 9 2014 13:24:19) [0]: Iris Pro, 1536 MB, OpenCL Version: 1.2 Compute Device: [0]
Intel Xeon Phi Coprocessor (OpenCL)
ArrayFire v2.1 (OpenCL, 64-bit Linux, build 4b9115c) License: Standalone (/home/arrayfire/.arrayfire.lic) Addons: MGL4, DLA, SLA Platform: Intel(R) OpenCL, Driver: 1.2.0.82248 [0]: Intel(R) Many Integrated Core Acceleration Card, 11634 MB, OpenCL Version: 1.2 1 : Genuine Intel(R) CPU @ 2.60GHz, 64372 MB, OpenCL Version: 1.2 Compute Device: [0]
NVIDIA GPUs (CUDA)
ArrayFire v2.1 (CUDA, 64-bit Linux, build 4b9115c) License: Standalone (/home/arrayfire/.arrayfire.lic) Addons: MGL4, DLA, SLA Platform: CUDA toolkit 6.0, Driver: 340.21 [0]: Tesla K40c, 11520 MB, CUDA Compute 3.5 1 : Quadro K5000, 4096 MB, CUDA Compute 3.0 Compute Device: [0], Display Device: [1] Memory Usage: 11406 MB free (11520 MB total)
NVIDIA GPUs (OpenCL)
ArrayFire v2.1 (OpenCL, 64-bit Linux, build 4b9115c) License: Standalone (/home/arrayfire/.arrayfire.lic) Addons: MGL4, DLA, SLA Platform: NVIDIA CUDA, Driver: 340.21 [0]: Tesla K40c, 11519 MB, OpenCL Version: 1.1 1 : Quadro K5000, 4095 MB, OpenCL Version: 1.1 Compute Device: [0]
Embedded Processors
ARM Mali GPU (OpenCL) #
ArrayFire v2.1 (OpenCL, 32-bit Linux, build 1971d38) License: Standalone (ARM Mali) Addons: MGL16, DLA, SLA Platform: ARM Platform, Driver: 1.1 [0]: Mali-T604, 1998 MB, OpenCL Version: 1.1
NVIDIA Tegra K1 (CUDA)
ArrayFire v2.1 (CUDA, 32-bit Linux, build 4c3c009) License: Standalone (Tegra) Addons: MGL16, DLA, SLA ARM 0 : GK20A, 1747 MB, CUDA Compute 3.2 Memory Usage: 106 MB free (1747 MB total)
Qualcomm Snapdragon SoC (OpenCL) #
ArrayFire v2.1 (OpenCL, 32-bit Linux, build bedc9ca) License: Standalone Addons: MGL16, DLA, SLA [0]: QUALCOMM Adreno(TM), 1389 MB, OpenCL Version: 1.1 1 : QUALCOMM Krait(TM), 1389 MB, OpenCL Version: 1.1 Compute Device: [0]
#: Experimental versions. Email technical@arrayfire.com for access.
The devices shown above are ones we have in-house for demonstration purposes. This is not an exhaustive list.
If you have OpenCL working on any other device and wish to try ArrayFire, contact us at technical@arrayfire.com.
Comments 2
Hi,
Where is your Google+ page?
It would be easier following you that way.
By the way,
I’d like to see benefits of OpenCL code on Intel / AMD CPU versus naive C code for the same task (Image convolution for instance).
But let the compiler do optimizations (Better with Intel Compiler).
Thank You.
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