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Remove logs, reduce code

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This commit is contained in:
Christoph Kluge 2023-11-24 17:22:06 +01:00
parent e34623b1ce
commit b8213ef6be
1 changed files with 80 additions and 166 deletions
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230
web/frontend/src/JobFootprint.svelte
View File

@ -10,57 +10,25 @@
Tooltip
} from "sveltestrap";
import { mean, round } from 'mathjs'
// import { formatNumber, scaleNumbers } from './units.js'
export let job
export let jobMetrics
export let view = 'job'
export let width = 'auto'
const isAcceleratedJob = (job.numAcc !== 0)
const isSharedJob = (job.exclusive !== 1)
console.log('JOB', job)
console.log('ACCELERATED?', isAcceleratedJob)
console.log('SHARED?', isSharedJob)
const clusters = getContext('clusters')
const subclusterConfig = clusters.find((c) => c.name == job.cluster).subClusters.find((sc) => sc.name == job.subCluster)
console.log('SCC', subclusterConfig)
/* NOTES:
- 'mem_allocated' für shared jobs (noch todo / nicht in den jobdaten enthalten bisher)
> For now: 'acc_util' gegen 'mem_used' für alex: Mem bw für shared weggefallen: dann wieder vier bars
- Energy Metric Missiing, muss eingebaut werden
- footprintMetrics Config in config.json?
*/
const footprintMetrics = isAcceleratedJob
? isSharedJob
const footprintMetrics = (job.numAcc !== 0)
? (job.exclusive !== 1)
? ['cpu_load', 'flops_any', 'acc_utilization']
: ['cpu_load', 'flops_any', 'acc_utilization', 'mem_bw']
: isSharedJob
: (job.exclusive !== 1)
? ['cpu_load', 'flops_any', 'mem_used']
: ['cpu_load', 'flops_any', 'mem_used', 'mem_bw']
console.log('JMs', jobMetrics.filter((jm) => footprintMetrics.includes(jm.name)))
const footprintMetricConfigs = footprintMetrics.map((fm) => {
return getContext('metrics')(job.cluster, fm)
}).filter( Boolean ) // Filter only "truthy" vals, see: https://stackoverflow.com/questions/28607451/removing-undefined-values-from-array
console.log("FMCs", footprintMetricConfigs)
const footprintMetricThresholds = footprintMetricConfigs.map((fmc) => {
return {name: fmc.name, ...findJobThresholds(fmc, job, subclusterConfig)}
}).filter( Boolean )
console.log("FMTs", footprintMetricThresholds)
const footprintData = footprintMetrics.map((fm) => {
const jm = jobMetrics.find((jm) => jm.name === fm && jm.scope === 'node')
// ... get Mean: Primarily use backend sourced avgs from job.*, secondarily calculate/read from metricdata
// Mean: Primarily use backend sourced avgs from job.*, secondarily calculate/read from metricdata
let mv = null
if (fm === 'cpu_load' && job.loadAvg !== 0) {
mv = round(job.loadAvg, 2)
@ -68,7 +36,9 @@
mv = round(job.flopsAnyAvg, 2)
} else if (fm === 'mem_bw' && job.memBwAvg !== 0) {
mv = round(job.memBwAvg, 2)
} else if (jm?.metric?.statisticsSeries) {
} else { // Calculate from jobMetrics
const jm = jobMetrics.find((jm) => jm.name === fm && jm.scope === 'node')
if (jm?.metric?.statisticsSeries) {
mv = round(mean(jm.metric.statisticsSeries.mean), 2)
} else if (jm?.metric?.series?.length > 1) {
const avgs = jm.metric.series.map(jms => jms.statistics.avg)
@ -76,86 +46,80 @@
} else {
mv = jm.metric.series[0].statistics.avg
}
// ... get Unit
}
// Unit
const fmc = getContext('metrics')(job.cluster, fm)
let unit = null
if (jm?.metric?.unit?.base) {
unit = jm.metric.unit.prefix + jm.metric.unit.base
if (fmc?.unit?.base) {
unit = fmc.unit.prefix + fmc.unit.base
} else {
unit = ''
}
// Get Threshold Limits from scaled Thresholds per Metric
const scaledThresholds = footprintMetricThresholds.find((fmc) => fmc.name === fm)
const levelPeak = fm === 'flops_any' ? round((scaledThresholds.peak * 0.85), 0) - mv : scaledThresholds.peak - mv // Scale flops_any down
const levelNormal = scaledThresholds.normal - mv
const levelCaution = scaledThresholds.caution - mv
const levelAlert = scaledThresholds.alert - mv
// Threshold / -Differences
const fmt = findJobThresholds(job, fmc, subclusterConfig)
const levelPeak = fm === 'flops_any' ? round((fmt.peak * 0.85), 0) - mv : fmt.peak - mv // Scale flops_any down
const levelNormal = fmt.normal - mv
const levelCaution = fmt.caution - mv
const levelAlert = fmt.alert - mv
// Define basic data
const fmBase = {
name: fm,
unit: unit,
avg: mv,
max: fm === 'flops_any' ? round((fmt.peak * 0.85), 0) : fmt.peak
}
// Collect
if (fm !== 'mem_used') { // Alert if usage is low, peak as maxmimum possible (scaled down for flops_any)
if (levelAlert > 0) {
return {
name: fm,
unit: unit,
avg: mv,
max: fm === 'flops_any' ? round((scaledThresholds.peak * 0.85), 0) : scaledThresholds.peak,
...fmBase,
color: 'danger',
message: 'Metric strongly below common levels!',
impact: 3
}
} else if (levelCaution > 0) {
return {
name: fm,
unit: unit,
avg: mv,
max: fm === 'flops_any' ? round((scaledThresholds.peak * 0.85), 0) : scaledThresholds.peak,
...fmBase,
color: 'warning',
message: 'Metric below common levels',
impact: 2
}
} else if (levelNormal > 0) {
return {
name: fm,
unit: unit,
avg: mv,
max: fm === 'flops_any' ? round((scaledThresholds.peak * 0.85), 0) : scaledThresholds.peak,
...fmBase,
color: 'success',
message: 'Metric within common levels',
impact: 1
}
} else if (levelPeak > 0) {
return {
name: fm,
unit: unit,
avg: mv,
max: fm === 'flops_any' ? round((scaledThresholds.peak * 0.85), 0) : scaledThresholds.peak,
...fmBase,
color: 'info',
message: 'Metric performs better than common levels',
impact: 0
}
} else { // Possible artifacts - <5% Margin OK, >5% warning, > 50% danger
const checkData = {
name: fm,
unit: unit,
avg: mv,
max: fm === 'flops_any' ? round((scaledThresholds.peak * 0.85), 0) : scaledThresholds.peak
}
if (checkData.avg >= (1.5 * checkData.max)) {
if (fmBase.avg >= (1.5 * fmBase.max)) {
return {
...checkData,
...fmBase,
color: 'secondary',
message: 'Metric average at least 50% above common peak value: Check data for artifacts!',
impact: -2
}
} else if (checkData.avg >= (1.05 * checkData.max)) {
} else if (fmBase.avg >= (1.05 * fmBase.max)) {
return {
...checkData,
...fmBase,
color: 'secondary',
message: 'Metric average at least 5% above common peak value: Check data for artifacts',
impact: -1
}
} else {
return {
...checkData,
...fmBase,
color: 'info',
message: 'Metric performs better than common levels',
impact: 0
@ -164,29 +128,23 @@
}
} else { // Inverse Logic: Alert if usage is high, Peak is bad and limits execution
if (levelPeak <= 0 && levelAlert <= 0 && levelCaution <= 0 && levelNormal <= 0) { // Possible artifacts - <5% Margin OK, >5% warning, > 50% danger
const checkData = {
name: fm,
unit: unit,
avg: mv,
max: scaledThresholds.peak
}
if (checkData.avg >= (1.5 * checkData.max)) {
if (fmBase.avg >= (1.5 * fmBase.max)) {
return {
...checkData,
...fmBase,
color: 'secondary',
message: 'Memory usage at least 50% above possible maximum value: Check data for artifacts!',
impact: -2
}
} else if (checkData.avg >= (1.05 * checkData.max)) {
} else if (fmBase.avg >= (1.05 * fmBase.max)) {
return {
...checkData,
...fmBase,
color: 'secondary',
message: 'Memory usage at least 5% above possible maximum value: Check data for artifacts!',
impact: -1
}
} else {
return {
...checkData,
...fmBase,
color: 'danger',
message: 'Memory usage extremely above common levels!',
impact: 4
@ -194,109 +152,72 @@
}
} else if (levelAlert <= 0 && levelCaution <= 0 && levelNormal <= 0) {
return {
name: fm,
unit: unit,
avg: mv,
max: scaledThresholds.peak,
...fmBase,
color: 'danger',
message: 'Memory usage extremely above common levels!',
impact: 4
}
} else if (levelAlert > 0 && (levelCaution <= 0 && levelNormal <= 0)) {
return {
name: fm,
unit: unit,
avg: mv,
max: scaledThresholds.peak,
...fmBase,
color: 'danger',
message: 'Memory usage strongly above common levels!',
impact: 3
}
} else if (levelCaution > 0 && levelNormal <= 0) {
return {
name: fm,
unit: unit,
avg: mv,
max: scaledThresholds.peak,
...fmBase,
color: 'warning',
message: 'Memory usage above common levels',
impact: 2
}
} else {
return {
name: fm,
unit: unit,
avg: mv,
max: scaledThresholds.peak,
...fmBase,
color: 'success',
message: 'Memory usage within common levels',
impact: 1
}
}
}
}).filter( Boolean )
console.log("FPD", footprintData)
})
</script>
<script context="module">
export function findJobThresholds(metricConfig, job, subClusterConfig) {
export function findJobThresholds(job, metricConfig, subClusterConfig) {
console.log('Hello', metricConfig.name, '@', subClusterConfig.name)
if (!metricConfig || !job || !subClusterConfig) {
if (!job || !metricConfig || !subClusterConfig) {
console.warn('Argument missing for findJobThresholds!')
return null
}
let subclusterThresholds = metricConfig.subClusters.find(sc => sc.name == subClusterConfig.name)
const subclusterThresholds = metricConfig.subClusters.find(sc => sc.name == subClusterConfig.name)
const defaultThresholds = {
peak: subclusterThresholds ? subclusterThresholds.peak : metricConfig.peak,
normal: subclusterThresholds ? subclusterThresholds.normal : metricConfig.normal,
caution: subclusterThresholds ? subclusterThresholds.caution : metricConfig.caution,
alert: subclusterThresholds ? subclusterThresholds.alert : metricConfig.alert
}
if (job.exclusive === 1) { // Exclusive: Use as defined
console.log('Job is exclusive: Use as defined')
if (subclusterThresholds) {
console.log('subClusterThresholds found: use subCluster specific thresholds', subclusterThresholds)
return defaultThresholds
} else { // Shared: Handle specifically
if (metricConfig.name === 'cpu_load') { // Special: Avg Aggregation BUT scaled based on #hwthreads
return {
peak: subclusterThresholds.peak,
normal: subclusterThresholds.normal,
caution: subclusterThresholds.caution,
alert: subclusterThresholds.alert
}
}
return {
peak: metricConfig.peak,
normal: metricConfig.normal,
caution: metricConfig.caution,
alert: metricConfig.alert
}
} else { // Shared
if (metricConfig.aggregation === 'avg' ){
console.log('metric uses "average" aggregation method: use unscaled thresholds except if cpu_load')
if (subclusterThresholds) {
console.log('subClusterThresholds found: use subCluster specific thresholds', subclusterThresholds)
console.log('PEAK/NORMAL USED', metricConfig.name === 'cpu_load' ? job.numHWThreads : subclusterThresholds.peak)
return { // If 'cpu_load': Peak/Normal === #HWThreads, keep other thresholds
peak: metricConfig.name === 'cpu_load' ? job.numHWThreads : subclusterThresholds.peak,
normal: metricConfig.name === 'cpu_load' ? job.numHWThreads : subclusterThresholds.normal,
caution: subclusterThresholds.caution,
alert: subclusterThresholds.alert
}
}
console.log('PEAK/NORMAL USED', metricConfig.name === 'cpu_load' ? job.numHWThreads : metricConfig.peak)
return {
peak: metricConfig.name === 'cpu_load' ? job.numHWThreads : metricConfig.peak,
normal: metricConfig.name === 'cpu_load' ? job.numHWThreads : metricConfig.normal,
caution: metricConfig.caution,
alert: metricConfig.alert
peak: job.numHWThreads,
normal: job.numHWThreads,
caution: defaultThresholds.caution,
alert: defaultThresholds.alert
}
} else if (metricConfig.aggregation === 'avg' ){
return defaultThresholds
} else if (metricConfig.aggregation === 'sum' ){
const jobFraction = job.numHWThreads / subClusterConfig.topology.node.length
console.log('Fraction', jobFraction)
return {
peak: round((metricConfig.peak * jobFraction), 0),
normal: round((metricConfig.normal * jobFraction), 0),
caution: round((metricConfig.caution * jobFraction), 0),
alert: round((metricConfig.alert * jobFraction), 0)
peak: round((defaultThresholds.peak * jobFraction), 0),
normal: round((defaultThresholds.normal * jobFraction), 0),
caution: round((defaultThresholds.caution * jobFraction), 0),
alert: round((defaultThresholds.alert * jobFraction), 0)
}
} else {
console.warn('Missing or unkown aggregation mode (sum/avg) for metric:', metricConfig)
@ -310,7 +231,7 @@
{#if view === 'job'}
<CardHeader>
<CardTitle class="mb-0 d-flex justify-content-center">
Core Metrics Footprint {isSharedJob ? '(Scaled)' : ''}
Core Metrics Footprint
</CardTitle>
</CardHeader>
{/if}
@ -362,13 +283,6 @@
/>
</div>
{/each}
<!-- <hr class="mt-1 mb-2"/>
<ul>
<li>Load Avg {round(job.loadAvg, 2)}</li>
<li>Flops Any {round(job.flopsAnyAvg, 2)}</li>
<li>Mem Used Max {round(job.memUsedMax, 2)}</li>
<li>Mem BW Avg {round(job.memBwAvg, 2)}</li>
</ul> -->
{#if job?.metaData?.message}
<hr class="mt-1 mb-2"/>
{@html job.metaData.message}