- #Used macbook air 13 inch mid 2012 code#
- #Used macbook air 13 inch mid 2012 mac#
- #Used macbook air 13 inch mid 2012 windows#
Launch the Activity Monitor and look at the Architecture column: MacOS Big Sur has a way to know for what architecture a program has been compiled. GeeXLab for x86_64 can be downloaded from this link. GeeXLab for arm64 can be downloaded from this link. This shadertoy test shows us that the M1 GPU is not a toy. Let’s look at the theoretical FP32 performance of these GPUs:
#Used macbook air 13 inch mid 2012 mac#
– Apple M1 Mac mini (x86_64 via rosetta): 220 FPS – Apple M1 Mac mini (arm64 native code): 245 FPS
#Used macbook air 13 inch mid 2012 windows#
– GeForce RTX 2070 on Windows 10: 720 FPS Geexlab-demopack-shadertoy/gl-32-shadertoy-02/neural-stanford-bunny-5-kb/ This demo has been ported to GeeXLab and is available in the shadertoy demopack in the following folder: I chose a recent demo that gives some good results: Neural Stanford Bunny 5kb.
Let’s do another test: a shadertoy demo with a heavy pixel shader. This statement found on the Apple M1 homepage is true:Īnd thanks to Metal and M1, some of the most graphically demanding apps perform better under Rosetta 2 than they did running natively on previous Mac systems with integrated graphics.
#Used macbook air 13 inch mid 2012 code#
Look at the framerate: 1951 FPS (M1 CPU, arm64 native code), 1282 FPS (M1 CPU, x86_64 emulated code via rosetta) and 471 FPS (Intel CPU, x86_64 native code). Here is the same GeeXLab on an Intel-based Mac mini: Rosetta works fine: here, we have GeeXLab compiled for x86_64 architecture running on Apple M1: Rosetta 2 is the new version for the switch from Intel to Apple silicon M1 CPUs. The first version of Rosetta was developed for the switch from PowerPC to Intel CPUs. Rosetta is a translation layer that allows the Apple M1 CPU (arm64 architecture) to execute a x86_64 code developed for Intel-based Macs. Fortunately, it’s easy, you are guided by macOS: On the Apple M1, you can’t launch an x86_64 executable directly. I compiled GeeXLab in two separate versions: GeeXLab for arm64 and GeeXLab for Intel x86_64. This is GeeXLab compiled for arm64 architecture: On Linux, the launch of GeeXLab is fast but on macOS Big Sur + Apple M1, the startup is a bit faster. Once I finished to build GeeXLab, I was impressed by its launch speed: very fast. The arm64-apple-macos11 target is handy if you are on an Intel-based Mac mini and want to compile for ARM architecture. Gcc main.c -o arm_test -target arm64-apple-macos11 If you want to generate a x86_64 code you have to use the following gcc option: By default on Apple M1 + Big Sur, gcc will generate an amr64 code. But in command line with gcc/g++, you have to specify the target architecture. Using Xcode 12.3, you can generate a kind of hybrid executable (Apple calls it an universal macOS binary) that packs the x86_64 and arm64 code. OpenGL is still there in the latest macOS Big Sur! I was surprised to see that all GeeXLab OpenGL code has been successfully compiled. By default, the compilation in command line (like gcc file.c) produces a binary for the arm64 architecture. I installed the latest Xcode 12.3 as well as the command line tools (because gcc/g++ are needed). The source code of GeeXLab is based on makefiles and I had no problem to compile all core libraries from the command line. See THIS PAGE for a more complete TFLOP table. This is more or less the same performance than the Radeon RX 560 or the GeForce GTX 1650: The M1 GPU has a FP32 performance of 2.6 TFLOPS ( source). The M1 chip is a SoC (System on a Chip) and includes a 8-core CPU, a 8-core GPU as well as other components like a neural engine, memory or input/output interfaces.
Here are some feedbacks about the Mac mini M1 I purchased few weeks ago, in order to compile and test GeeXLab with the new Apple Silicon M1.
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