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Like I Knew What I am Doing
LIKWID.jl is a Julia wrapper for the performance monitoring and benchmarking suite LIKWID.
Talk (25 min) given at JuliaCon 2022.
Prerequisites:
- You must have
likwidinstalled (see the build & install instructions). - You must be running Linux. (LIKWID doesn't support macOS or Windows.)
LIKWID.jl is a registered Julia package. Hence, you can simply add it to your Julia environment with the command
] add LIKWIDPlease check out the documentation to learn how to use LIKWID.jl.
using LIKWID
N = 10_000
a = 3.141
x = rand(N)
y = rand(N)
z = zeros(N)
function daxpy!(z, a, x, y)
z .= a .* x .+ y
end
daxpy!(z, a, x, y); # warmup
metrics, events = @perfmon "FLOPS_DP" daxpy!(z, a, x, y); # double-precision floating point ops.Output:
Group: FLOPS_DP
┌───────────────────────────┬──────────┐
│ Event │ Thread 1 │
├───────────────────────────┼──────────┤
│ ACTUAL_CPU_CLOCK │ 73956.0 │
│ MAX_CPU_CLOCK │ 51548.0 │
│ RETIRED_INSTRUCTIONS │ 10357.0 │
│ CPU_CLOCKS_UNHALTED │ 23174.0 │
│ RETIRED_SSE_AVX_FLOPS_ALL │ 20000.0 │
│ MERGE │ 0.0 │
└───────────────────────────┴──────────┘
┌──────────────────────┬────────────┐
│ Metric │ Thread 1 │
├──────────────────────┼────────────┤
│ Runtime (RDTSC) [s] │ 7.68048e-6 │
│ Runtime unhalted [s] │ 3.0188e-5 │
│ Clock [MHz] │ 3514.8 │
│ CPI │ 2.23752 │
│ DP [MFLOP/s] │ 2604.0 │
└──────────────────────┴────────────┘
- LIKWID / LIKWID Performance Tools
- Most C-bindings have been autogenerated using Clang.jl
- pylikwid: Python wrappers of LIKWID
- Logo by @davibarreira
- The Erlangen National High Performance Computing Center (NHR@FAU) supports the project with CI infrastructure
LIKWID.jl is an effort by the Paderborn Center for Parallel Computing (PC²) and, originally, the MIT JuliaLab.