kuhar

#!/usr/bin/env python3
# Copyright (c) 2026 Advanced Micro Devices, Inc.
# SPDX-License-Identifier: MIT

"""Compare RocJITsu and HotSwap gfx1250 B0-to-A0 translation resources.

Each translator runs in a fresh process. Linux wait4 resource accounting
captures user plus system CPU time and peak resident set size for that process.
The runner enables native translator timers by default and normalizes them into
exclusive common phase buckets using monotonic wall time.

kuhar

# cursor-tmux.sh -- Cursor IDE <-> tmux integration for Claude Code
#
# When sourced inside Cursor's terminal, snapshots IDE env vars to a file.
# When sourced elsewhere (tmux, other shells), restores them.
# The claude() wrapper strips vars that trigger a PID ancestry check.
#
# Supports multiple Cursor instances: env files are stored per-port
# in ~/.cursor-env.d/. Use cursor-select to switch instances.

_CURSOR_ENV_DIR="$HOME/.cursor-env.d"

kuhar

/home/jakub/llvm/test/src/mlir/lib/Interfaces/DataLayoutInterfaces.cpp:392:10: warning: redundant explicit template argument [llvm-type-switch-case-types]
  392 |         .Case<ModuleOp>([&](ModuleOp op) {
/home/jakub/llvm/test/src/mlir/lib/Interfaces/DataLayoutInterfaces.cpp:404:10: warning: redundant explicit template argument [llvm-type-switch-case-types]
  404 |         .Case<DataLayoutOpInterface>([&](DataLayoutOpInterface op) {
/home/jakub/llvm/test/src/mlir/lib/Conversion/PDLToPDLInterp/PredicateTree.cpp:353:10: warning: lambda parameter needlessly uses 'auto', use explicit type instead [llvm-type-switch-case-types]
  353 |         .Case<pdl::ApplyNativeConstraintOp>([&](auto constraintOp) {
/home/jakub/llvm/test/src/mlir/lib/Conversion/PDLToPDLInterp/PredicateTree.cpp:353:49: note: replace 'auto' with explicit type
  353 |         .Case<pdl::ApplyNativeConstraintOp>([&](auto constraintOp) {
/home/jakub/llvm/test/src/mlir/lib/Conversion/PDLToPDLInterp/PredicateTree.cpp:353:15: note: type from template 

kuhar

echo 0 | sudo tee /proc/sys/kernel/perf_event_paranoid

sudo perf record -F 999 -g -- tools/iree-compile --iree-hal-target-backends=rocm --iree-rocm-target-chip=gfx942 stable_diffusion_xl_base_1_0_64_1024x1024_fp16_unet_linalg_nithin.mlir --iree-preprocessing-transform-spec-filename=config-preprocessing.mlir
sudo chown "$USER:$USER" perf.data

pprof -http : perf.data

kuhar

---
Language:        Cpp
# BasedOnStyle:  Google
AccessModifierOffset: -1
AlignAfterOpenBracket: Align
AlignArrayOfStructures: None
AlignConsecutiveAssignments:
  Enabled:         false
  AcrossEmptyLines: false
  AcrossComments:  false

kuhar

// -----// IR Dump After TypePropagation (iree-codegen-type-propagation) //----- //
func.func @_reverse_dim0_dispatch_0() {
  %c0 = arith.constant 0 : index
  %0 = hal.interface.binding.subspan set(0) binding(0) type(storage_buffer) alignment(64) offset(%c0) flags(ReadOnly) : !flow.dispatch.tensor<readonly:tensor<2x1xf32>>
  %1 = hal.interface.binding.subspan set(0) binding(1) type(storage_buffer) alignment(64) offset(%c0) : !flow.dispatch.tensor<writeonly:tensor<2x1xf32>>
  %2 = flow.dispatch.tensor.load %0, offsets = [0, 0], sizes = [2, 1], strides = [1, 1] : !flow.dispatch.tensor<readonly:tensor<2x1xf32>> -> tensor<2x1xf32>
  %3 = tensor.empty() : tensor<2x1xf32>
  %4 = iree_linalg_ext.reverse dimensions(dense<0> : tensor<1xi64>) ins(%2 : tensor<2x1xf32>) outs(%3 : tensor<2x1xf32>) : tensor<2x1xf32>
  flow.dispatch.tensor.store %4, %1, offsets = [0, 0], sizes = [2, 1], strides = [1, 1] : tensor<2x1xf32> -> !flow.dispatch.tensor<writeonly:tensor<2x1xf32>>
  return

kuhar

// -----// IR Dump After TosaToSCF (tosa-to-scf) //----- //
func.func @main() {
  %0 = util.unfoldable_constant dense<1> : tensor<1024x1024xi8>
  %1 = util.unfoldable_constant dense<1> : tensor<1024x1024xi8>
  %c0_i32 = arith.constant 0 : i32
  %c0 = arith.constant 0 : index
  %2 = tensor.empty() : tensor<1024x1024xi32>
  %3 = linalg.fill ins(%c0_i32 : i32) outs(%2 : tensor<1024x1024xi32>) -> tensor<1024x1024xi32>
  %4 = linalg.matmul ins(%0, %1 : tensor<1024x1024xi8>, tensor<1024x1024xi8>) outs(%3 : tensor<1024x1024xi32>) -> tensor<1024x1024xi32>
  check.expect_eq_const(%4, dense<1024> : tensor<1024x1024xi32>) : tensor<1024x1024xi32>

kuhar

cmake_minimum_required(VERSION 2.8.4)
project(ean_logic)

set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++1y -g -DNDEBUG=1")

set(
	SOURCE_FILES example.cpp
	IntervalArithmetic.cpp
	Interval.cpp
	)

kuhar

#include <ratio>

template <char... Ts>
struct seq;

template <class Seq>
struct parse_sign
{
	static constexpr auto sign = intmax_t{1};
	using tail = Seq;

kuhar

#include <iostream>
#include <type_traits>
#include <tuple>
using namespace std;


namespace impl
{

	template <typename Functor, typename Tuple, size_t... Index>