tkf

tkf

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tkf

Analyzing parallel matrix-matrix multiplication DAG

See https://gist.github.com/tkf/44a71bfb26f27efafa2edafbc0c87092 for the report output.

This is a set of scripts for using TaskDAGAnalyzers.jl to analyze the matrix-matrix multiplication in More balanced task DAG by tkf · Pull Request #83 · MasonProtter/Gaius.jl.

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Gaius benchmarks

This is a benchmark driver for More balanced task DAG by tkf · Pull Request #83 · MasonProtter/Gaius.jl

Usage

make BENCHMARKS=bench_mat_vec_mul.jl all  # benchmark matrix-vector multiplication
make BENCHMARKS=bench_mat_mat_mul.jl all # benchmark matrix-matrix multiplication

tkf

Analysis of parallel matrix-matrix multiplication DAG

Target summary

size(A) = (256, 256)

TaskDAGAnalyzers.summary:
work: 1.6 ms (single-thread run-time T₁)
span: 109 μs (theoretical fastest run-time Tₒₒ)

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diff --git a/llvm/lib/CodeGen/DwarfEHPrepare.cpp b/llvm/lib/CodeGen/DwarfEHPrepare.cpp
index 5ca1e91cc5f4..fde7b942665d 100644
--- a/llvm/lib/CodeGen/DwarfEHPrepare.cpp
+++ b/llvm/lib/CodeGen/DwarfEHPrepare.cpp
@@ -1,350 +1,355 @@
 //===- DwarfEHPrepare - Prepare exception handling for code generation ----===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception

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julia> Threads.nthreads()
16

julia> ENV["JULIA_EXCLUSIVE"]
"1"

julia> suite = SyncBarriersBenchmarks.BenchUniformLoops.setup();

julia> results = run(suite["dissemination"]["spin"])
2-element BenchmarkTools.BenchmarkGroup:

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*.pyc
.pytest_cache

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# This file is machine-generated - editing it directly is not advised

[[AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "485ee0867925449198280d4af84bdb46a2a404d0"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.0.1"

[[Adapt]]
deps = ["LinearAlgebra"]