ItzLevvie

Android Canary (202511) — ZP11.251031.009 (14396972)

Device	Flash	Download	SHA-256 Checksum
Pixel 6 (oriole)	Link	Link	090145837d44224448311b65ec98c9af32890dd499ce64c3c657b5319a9643c9
Pixel 6 Pro (raven)	Link	Link	b84d3368c74ad579c125cfc60597209236a730a2a7bbd776119d4dcc9008038f
Pixel 6a (bluejay)	Link	Link	9ac60f986b386847878a3d531d1f6a75c43d8c7ccb900ccb5

fxkamd

This is Felix Kuehling, long time KFD driver architect. I started looking into the TinyGrad source code yesterday, focusing on ops_kfd.py, ops_hsa.py and driver/hsa.py, to understand how TinyGrad talks to our HW and help with the ongoing debugging effort from the top down. This analysis is based on this commit: https://github.com/tinygrad/tinygrad/tree/3de855ea50d72238deac14fc05cda2a611497778

I'm intrigued by the use of Python for low-level programming. I think I can learn something from your use of ctypes and clang2py for fast prototyping and test development. I want to share some observations based on my initial review.

ops_kfd looks pretty new, and I see many problems with it based on my long experience working on KFD. I think it's interesting, but probably not relevant for the most pressing problems at hand, so I'll cover that last.

ops_hsa uses ROCr APIs to manage GPU memory, create a user mode AQL queue for GPU kernel dispatch, async SDMA copies, and signal-based synchronization with barrier packets

briansp2020

(tf) root@rocm:~/tmp# python benchmark.py
2023-10-14 15:02:22.116047: E external/local_xla/xla/stream_executor/plugin_registry.cc:93] Invalid plugin kind specified: DNN
2023-10-14 15:02:22.348480: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-10-14 15:02:23.756833: I external/local_xla/xla/stream_executor/rocm/rocm_gpu_executor.cc:787] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-10-14 15:02:23.982269: I external/local_xla/xla/stream_executor/rocm/rocm_gpu_executor.cc:787] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-10-14 15:02:23.9823

timdecode

//  Created by Timothy Davison on 2023-06-21.
//
// This is a Metal implementation of subgroupPartitionNV. You use it to find a mask of
// the other threads in a simd-group with the same value (a partition of the simd-group about
// a set of values). 
//
// Feel free to use this in your code. Please share any fixes or ideas to make it faster.
// 
// Khronos docs on subgroup partitioning:
// - https://github.com/KhronosGroup/GLSL/blob/master/extensions/nv/GL_NV_shader_subgroup_partitioned.txt

zingaburga

ARM’s Scalable Vector Extensions: A Critical Look at SVE2 For Integer Workloads

Scalable Vector Extensions (SVE) is ARM’s latest SIMD extension to their instruction set, which was announced back in 2016. A follow-up SVE2 extension was announced in 2019, designed to incorporate all functionality from ARM’s current primary SIMD extension, NEON (aka ASIMD).

Despite being announced 5 years ago, there is currently no generally available CPU which supports any form of SVE (which excludes the [Fugaku supercomputer](https://www.fujitsu.com/global/about/innovation/

woachk

% ./clpeak                                                                                 
[mvk-info] MoltenVK version 1.1.5, supporting Vulkan version 1.1.189.
	The following 72 Vulkan extensions are supported:
		VK_KHR_16bit_storage v1
		VK_KHR_8bit_storage v1
		VK_KHR_bind_memory2 v1
		VK_KHR_create_renderpass2 v1
		VK_KHR_dedicated_allocation v3
		VK_KHR_depth_stencil_resolve v1
		VK_KHR_descriptor_update_template v1

dougallj

# IDA (disassembler) and Hex-Rays (decompiler) plugin for Apple AMX
#
# WIP research. (This was edited to add more info after someone posted it to
# Hacker News. Click "Revisions" to see full changes.)
#
# Copyright (c) 2020 dougallj


# Based on Python port of VMX intrinsics plugin:
# Copyright (c) 2019 w4kfu - Synacktiv

citruz

Running Linux and Windows on M1 with QEMU

30.11.2020: Updated with the new patchseries and instructions for Windows

02.12.2020: Added tweaks

08.12.2020: Updated with patchseries v4

31.01.2020: Updated with patchseries v6

niw

How to run Windows 10 on ARM or Ubuntu for ARM64 in QEMU on Apple Silicon Mac

Here is easy steps to try Windows 10 on ARM or Ubuntu for ARM64 on your Apple Silicon Mac. Enjoy!

NOTE: that this is current, 10/1/2021 state.

Running Windows 10 on ARM

1. Install Xcode from App Store or install Command Line Tools on your Mac

stblr

platform: 7.5
ext: 7p5
name: HSW
1 add add 0x40 Addition 
    0xfc0 u8 i8 u16 i16 u32 i32 , 0xfc0 u8 i8 u16 i16 u32 i32 
    0x20000 f32 , 0xfc0 u8 i8 u16 i16 u32 i32 
    0x20000 f32 , 0x20000 f32 
    0x40000 f64 , 0x40000 f64 
3 addc addc 0x4e Addition with Carry 
    0x400 u32 , 0x400 u32