cc-metric-collector/collectors/nvidiaMetric.go

1170 lines
40 KiB
Go

package collectors
import (
"encoding/json"
"errors"
"fmt"
"log"
"time"
cclog "github.com/ClusterCockpit/cc-metric-collector/internal/ccLogger"
lp "github.com/ClusterCockpit/cc-metric-collector/internal/ccMetric"
"github.com/NVIDIA/go-nvml/pkg/nvml"
)
type NvidiaCollectorConfig struct {
ExcludeMetrics []string `json:"exclude_metrics,omitempty"`
ExcludeDevices []string `json:"exclude_devices,omitempty"`
AddPciInfoTag bool `json:"add_pci_info_tag,omitempty"`
UsePciInfoAsTypeId bool `json:"use_pci_info_as_type_id,omitempty"`
AddUuidMeta bool `json:"add_uuid_meta,omitempty"`
AddBoardNumberMeta bool `json:"add_board_number_meta,omitempty"`
AddSerialMeta bool `json:"add_serial_meta,omitempty"`
ProcessMigDevices bool `json:"process_mig_devices,omitempty"`
}
type NvidiaCollectorDevice struct {
device nvml.Device
excludeMetrics map[string]bool
tags map[string]string
meta map[string]string
}
type NvidiaCollector struct {
metricCollector
config NvidiaCollectorConfig
gpus []NvidiaCollectorDevice
num_gpus int
}
func (m *NvidiaCollector) CatchPanic() {
if rerr := recover(); rerr != nil {
log.Print(rerr)
m.init = false
}
}
func (m *NvidiaCollector) Init(config json.RawMessage) error {
var err error
m.name = "NvidiaCollector"
m.config.AddPciInfoTag = false
m.config.UsePciInfoAsTypeId = false
m.setup()
if len(config) > 0 {
err = json.Unmarshal(config, &m.config)
if err != nil {
return err
}
}
m.meta = map[string]string{
"source": m.name,
"group": "Nvidia",
}
defer m.CatchPanic()
// Initialize NVIDIA Management Library (NVML)
ret := nvml.Init()
if ret != nvml.SUCCESS {
err = errors.New(nvml.ErrorString(ret))
cclog.ComponentError(m.name, "Unable to initialize NVML", err.Error())
return err
}
// Number of NVIDIA GPUs
num_gpus, ret := nvml.DeviceGetCount()
if ret != nvml.SUCCESS {
err = errors.New(nvml.ErrorString(ret))
cclog.ComponentError(m.name, "Unable to get device count", err.Error())
return err
}
// For all GPUs
idx := 0
m.gpus = make([]NvidiaCollectorDevice, num_gpus)
for i := 0; i < num_gpus; i++ {
// Skip excluded devices by ID
str_i := fmt.Sprintf("%d", i)
if _, skip := stringArrayContains(m.config.ExcludeDevices, str_i); skip {
cclog.ComponentDebug(m.name, "Skipping excluded device", str_i)
continue
}
// Get device handle
device, ret := nvml.DeviceGetHandleByIndex(i)
if ret != nvml.SUCCESS {
err = errors.New(nvml.ErrorString(ret))
cclog.ComponentError(m.name, "Unable to get device at index", i, ":", err.Error())
continue
}
// Get device's PCI info
pciInfo, ret := nvml.DeviceGetPciInfo(device)
if ret != nvml.SUCCESS {
err = errors.New(nvml.ErrorString(ret))
cclog.ComponentError(m.name, "Unable to get PCI info for device at index", i, ":", err.Error())
continue
}
// Create PCI ID in the common format used by the NVML.
pci_id := fmt.Sprintf(
nvml.DEVICE_PCI_BUS_ID_FMT,
pciInfo.Domain,
pciInfo.Bus,
pciInfo.Device)
// Skip excluded devices specified by PCI ID
if _, skip := stringArrayContains(m.config.ExcludeDevices, pci_id); skip {
cclog.ComponentDebug(m.name, "Skipping excluded device", pci_id)
continue
}
// Select which value to use as 'type-id'.
// The PCI ID is commonly required in SLURM environments because the
// numberic IDs used by SLURM and the ones used by NVML might differ
// depending on the job type. The PCI ID is more reliable but is commonly
// not recorded for a job, so it must be added manually in prologue or epilogue
// e.g. to the comment field
tid := str_i
if m.config.UsePciInfoAsTypeId {
tid = pci_id
}
// Now we got all infos together, populate the device list
g := &m.gpus[idx]
// Add device handle
g.device = device
// Add tags
g.tags = map[string]string{
"type": "accelerator",
"type-id": tid,
}
// Add PCI info as tag if not already used as 'type-id'
if m.config.AddPciInfoTag && !m.config.UsePciInfoAsTypeId {
g.tags["pci_identifier"] = pci_id
}
g.meta = map[string]string{
"source": m.name,
"group": "Nvidia",
}
if m.config.AddBoardNumberMeta {
board, ret := nvml.DeviceGetBoardPartNumber(device)
if ret != nvml.SUCCESS {
cclog.ComponentError(m.name, "Unable to get boart part number for device at index", i, ":", err.Error())
} else {
g.meta["board_number"] = board
}
}
if m.config.AddSerialMeta {
serial, ret := nvml.DeviceGetSerial(device)
if ret != nvml.SUCCESS {
cclog.ComponentError(m.name, "Unable to get serial number for device at index", i, ":", err.Error())
} else {
g.meta["serial"] = serial
}
}
if m.config.AddUuidMeta {
uuid, ret := nvml.DeviceGetUUID(device)
if ret != nvml.SUCCESS {
cclog.ComponentError(m.name, "Unable to get UUID for device at index", i, ":", err.Error())
} else {
g.meta["uuid"] = uuid
}
}
// Add excluded metrics
g.excludeMetrics = map[string]bool{}
for _, e := range m.config.ExcludeMetrics {
g.excludeMetrics[e] = true
}
// Increment the index for the next device
idx++
}
m.num_gpus = idx
m.init = true
return nil
}
func readMemoryInfo(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_fb_mem_total"] || !device.excludeMetrics["nv_fb_mem_used"] || !device.excludeMetrics["nv_fb_mem_reserved"] {
var total uint64
var used uint64
var reserved uint64 = 0
var v2 bool = false
meminfo, ret := nvml.DeviceGetMemoryInfo(device.device)
if ret != nvml.SUCCESS {
err := errors.New(nvml.ErrorString(ret))
return err
}
total = meminfo.Total
used = meminfo.Used
if !device.excludeMetrics["nv_fb_mem_total"] {
t := float64(total) / (1024 * 1024)
y, err := lp.New("nv_fb_mem_total", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "MByte")
output <- y
}
}
if !device.excludeMetrics["nv_fb_mem_used"] {
f := float64(used) / (1024 * 1024)
y, err := lp.New("nv_fb_mem_used", device.tags, device.meta, map[string]interface{}{"value": f}, time.Now())
if err == nil {
y.AddMeta("unit", "MByte")
output <- y
}
}
if v2 && !device.excludeMetrics["nv_fb_mem_reserved"] {
r := float64(reserved) / (1024 * 1024)
y, err := lp.New("nv_fb_mem_reserved", device.tags, device.meta, map[string]interface{}{"value": r}, time.Now())
if err == nil {
y.AddMeta("unit", "MByte")
output <- y
}
}
}
return nil
}
func readBarMemoryInfo(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_bar1_mem_total"] || !device.excludeMetrics["nv_bar1_mem_used"] {
meminfo, ret := nvml.DeviceGetBAR1MemoryInfo(device.device)
if ret != nvml.SUCCESS {
err := errors.New(nvml.ErrorString(ret))
return err
}
if !device.excludeMetrics["nv_bar1_mem_total"] {
t := float64(meminfo.Bar1Total) / (1024 * 1024)
y, err := lp.New("nv_bar1_mem_total", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "MByte")
output <- y
}
}
if !device.excludeMetrics["nv_bar1_mem_used"] {
t := float64(meminfo.Bar1Used) / (1024 * 1024)
y, err := lp.New("nv_bar1_mem_used", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "MByte")
output <- y
}
}
}
return nil
}
func readUtilization(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
isMig, ret := nvml.DeviceIsMigDeviceHandle(device.device)
if ret != nvml.SUCCESS {
err := errors.New(nvml.ErrorString(ret))
return err
}
if !isMig {
return nil
}
if !device.excludeMetrics["nv_util"] || !device.excludeMetrics["nv_mem_util"] {
// Retrieves the current utilization rates for the device's major subsystems.
//
// Available utilization rates
// * Gpu: Percent of time over the past sample period during which one or more kernels was executing on the GPU.
// * Memory: Percent of time over the past sample period during which global (device) memory was being read or written
//
// Note:
// * During driver initialization when ECC is enabled one can see high GPU and Memory Utilization readings.
// This is caused by ECC Memory Scrubbing mechanism that is performed during driver initialization.
// * On MIG-enabled GPUs, querying device utilization rates is not currently supported.
util, ret := nvml.DeviceGetUtilizationRates(device.device)
if ret == nvml.SUCCESS {
if !device.excludeMetrics["nv_util"] {
y, err := lp.New("nv_util", device.tags, device.meta, map[string]interface{}{"value": float64(util.Gpu)}, time.Now())
if err == nil {
y.AddMeta("unit", "%")
output <- y
}
}
if !device.excludeMetrics["nv_mem_util"] {
y, err := lp.New("nv_mem_util", device.tags, device.meta, map[string]interface{}{"value": float64(util.Memory)}, time.Now())
if err == nil {
y.AddMeta("unit", "%")
output <- y
}
}
}
}
return nil
}
func readTemp(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_temp"] {
// Retrieves the current temperature readings for the device, in degrees C.
//
// Available temperature sensors:
// * TEMPERATURE_GPU: Temperature sensor for the GPU die.
// * NVML_TEMPERATURE_COUNT
temp, ret := nvml.DeviceGetTemperature(device.device, nvml.TEMPERATURE_GPU)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_temp", device.tags, device.meta, map[string]interface{}{"value": float64(temp)}, time.Now())
if err == nil {
y.AddMeta("unit", "degC")
output <- y
}
}
}
return nil
}
func readFan(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_fan"] {
// Retrieves the intended operating speed of the device's fan.
//
// Note: The reported speed is the intended fan speed.
// If the fan is physically blocked and unable to spin, the output will not match the actual fan speed.
//
// For all discrete products with dedicated fans.
//
// The fan speed is expressed as a percentage of the product's maximum noise tolerance fan speed.
// This value may exceed 100% in certain cases.
fan, ret := nvml.DeviceGetFanSpeed(device.device)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_fan", device.tags, device.meta, map[string]interface{}{"value": float64(fan)}, time.Now())
if err == nil {
y.AddMeta("unit", "%")
output <- y
}
}
}
return nil
}
// func readFans(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
// if !device.excludeMetrics["nv_fan"] {
// numFans, ret := nvml.DeviceGetNumFans(device.device)
// if ret == nvml.SUCCESS {
// for i := 0; i < numFans; i++ {
// fan, ret := nvml.DeviceGetFanSpeed_v2(device.device, i)
// if ret == nvml.SUCCESS {
// y, err := lp.New("nv_fan", device.tags, device.meta, map[string]interface{}{"value": float64(fan)}, time.Now())
// if err == nil {
// y.AddMeta("unit", "%")
// y.AddTag("stype", "fan")
// y.AddTag("stype-id", fmt.Sprintf("%d", i))
// output <- y
// }
// }
// }
// }
// }
// return nil
// }
func readEccMode(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_ecc_mode"] {
// Retrieves the current and pending ECC modes for the device.
//
// For Fermi or newer fully supported devices. Only applicable to devices with ECC.
// Requires NVML_INFOROM_ECC version 1.0 or higher.
//
// Changing ECC modes requires a reboot.
// The "pending" ECC mode refers to the target mode following the next reboot.
_, ecc_pend, ret := nvml.DeviceGetEccMode(device.device)
if ret == nvml.SUCCESS {
var y lp.CCMetric
var err error
switch ecc_pend {
case nvml.FEATURE_DISABLED:
y, err = lp.New("nv_ecc_mode", device.tags, device.meta, map[string]interface{}{"value": "OFF"}, time.Now())
case nvml.FEATURE_ENABLED:
y, err = lp.New("nv_ecc_mode", device.tags, device.meta, map[string]interface{}{"value": "ON"}, time.Now())
default:
y, err = lp.New("nv_ecc_mode", device.tags, device.meta, map[string]interface{}{"value": "UNKNOWN"}, time.Now())
}
if err == nil {
output <- y
}
} else if ret == nvml.ERROR_NOT_SUPPORTED {
y, err := lp.New("nv_ecc_mode", device.tags, device.meta, map[string]interface{}{"value": "N/A"}, time.Now())
if err == nil {
output <- y
}
}
}
return nil
}
func readPerfState(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_perf_state"] {
// Retrieves the current performance state for the device.
//
// Allowed PStates:
// 0: Maximum Performance.
// ..
// 15: Minimum Performance.
// 32: Unknown performance state.
pState, ret := nvml.DeviceGetPerformanceState(device.device)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_perf_state", device.tags, device.meta, map[string]interface{}{"value": fmt.Sprintf("P%d", int(pState))}, time.Now())
if err == nil {
output <- y
}
}
}
return nil
}
func readPowerUsage(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_power_usage"] {
// Retrieves power usage for this GPU in milliwatts and its associated circuitry (e.g. memory)
//
// On Fermi and Kepler GPUs the reading is accurate to within +/- 5% of current power draw.
//
// It is only available if power management mode is supported
mode, ret := nvml.DeviceGetPowerManagementMode(device.device)
if ret != nvml.SUCCESS {
return nil
}
if mode == nvml.FEATURE_ENABLED {
power, ret := nvml.DeviceGetPowerUsage(device.device)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_power_usage", device.tags, device.meta, map[string]interface{}{"value": float64(power) / 1000}, time.Now())
if err == nil {
y.AddMeta("unit", "watts")
output <- y
}
}
}
}
return nil
}
func readClocks(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
// Retrieves the current clock speeds for the device.
//
// Available clock information:
// * CLOCK_GRAPHICS: Graphics clock domain.
// * CLOCK_SM: Streaming Multiprocessor clock domain.
// * CLOCK_MEM: Memory clock domain.
if !device.excludeMetrics["nv_graphics_clock"] {
graphicsClock, ret := nvml.DeviceGetClockInfo(device.device, nvml.CLOCK_GRAPHICS)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_graphics_clock", device.tags, device.meta, map[string]interface{}{"value": float64(graphicsClock)}, time.Now())
if err == nil {
y.AddMeta("unit", "MHz")
output <- y
}
}
}
if !device.excludeMetrics["nv_sm_clock"] {
smCock, ret := nvml.DeviceGetClockInfo(device.device, nvml.CLOCK_SM)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_sm_clock", device.tags, device.meta, map[string]interface{}{"value": float64(smCock)}, time.Now())
if err == nil {
y.AddMeta("unit", "MHz")
output <- y
}
}
}
if !device.excludeMetrics["nv_mem_clock"] {
memClock, ret := nvml.DeviceGetClockInfo(device.device, nvml.CLOCK_MEM)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_mem_clock", device.tags, device.meta, map[string]interface{}{"value": float64(memClock)}, time.Now())
if err == nil {
y.AddMeta("unit", "MHz")
output <- y
}
}
}
if !device.excludeMetrics["nv_video_clock"] {
memClock, ret := nvml.DeviceGetClockInfo(device.device, nvml.CLOCK_VIDEO)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_video_clock", device.tags, device.meta, map[string]interface{}{"value": float64(memClock)}, time.Now())
if err == nil {
y.AddMeta("unit", "MHz")
output <- y
}
}
}
return nil
}
func readMaxClocks(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
// Retrieves the maximum clock speeds for the device.
//
// Available clock information:
// * CLOCK_GRAPHICS: Graphics clock domain.
// * CLOCK_SM: Streaming multiprocessor clock domain.
// * CLOCK_MEM: Memory clock domain.
// * CLOCK_VIDEO: Video encoder/decoder clock domain.
// * CLOCK_COUNT: Count of clock types.
//
// Note:
/// On GPUs from Fermi family current P0 clocks (reported by nvmlDeviceGetClockInfo) can differ from max clocks by few MHz.
if !device.excludeMetrics["nv_max_graphics_clock"] {
max_gclk, ret := nvml.DeviceGetMaxClockInfo(device.device, nvml.CLOCK_GRAPHICS)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_max_graphics_clock", device.tags, device.meta, map[string]interface{}{"value": float64(max_gclk)}, time.Now())
if err == nil {
y.AddMeta("unit", "MHz")
output <- y
}
}
}
if !device.excludeMetrics["nv_max_sm_clock"] {
maxSmClock, ret := nvml.DeviceGetClockInfo(device.device, nvml.CLOCK_SM)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_max_sm_clock", device.tags, device.meta, map[string]interface{}{"value": float64(maxSmClock)}, time.Now())
if err == nil {
y.AddMeta("unit", "MHz")
output <- y
}
}
}
if !device.excludeMetrics["nv_max_mem_clock"] {
maxMemClock, ret := nvml.DeviceGetClockInfo(device.device, nvml.CLOCK_MEM)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_max_mem_clock", device.tags, device.meta, map[string]interface{}{"value": float64(maxMemClock)}, time.Now())
if err == nil {
y.AddMeta("unit", "MHz")
output <- y
}
}
}
if !device.excludeMetrics["nv_max_video_clock"] {
maxMemClock, ret := nvml.DeviceGetClockInfo(device.device, nvml.CLOCK_VIDEO)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_max_video_clock", device.tags, device.meta, map[string]interface{}{"value": float64(maxMemClock)}, time.Now())
if err == nil {
y.AddMeta("unit", "MHz")
output <- y
}
}
}
return nil
}
func readEccErrors(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_ecc_uncorrected_error"] {
// Retrieves the total ECC error counts for the device.
//
// For Fermi or newer fully supported devices.
// Only applicable to devices with ECC.
// Requires NVML_INFOROM_ECC version 1.0 or higher.
// Requires ECC Mode to be enabled.
//
// The total error count is the sum of errors across each of the separate memory systems,
// i.e. the total set of errors across the entire device.
ecc_db, ret := nvml.DeviceGetTotalEccErrors(device.device, nvml.MEMORY_ERROR_TYPE_UNCORRECTED, nvml.AGGREGATE_ECC)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_ecc_uncorrected_error", device.tags, device.meta, map[string]interface{}{"value": float64(ecc_db)}, time.Now())
if err == nil {
output <- y
}
}
}
if !device.excludeMetrics["nv_ecc_corrected_error"] {
ecc_sb, ret := nvml.DeviceGetTotalEccErrors(device.device, nvml.MEMORY_ERROR_TYPE_CORRECTED, nvml.AGGREGATE_ECC)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_ecc_corrected_error", device.tags, device.meta, map[string]interface{}{"value": float64(ecc_sb)}, time.Now())
if err == nil {
output <- y
}
}
}
return nil
}
func readPowerLimit(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_power_max_limit"] {
// Retrieves the power management limit associated with this device.
//
// For Fermi or newer fully supported devices.
//
// The power limit defines the upper boundary for the card's power draw.
// If the card's total power draw reaches this limit the power management algorithm kicks in.
pwr_limit, ret := nvml.DeviceGetPowerManagementLimit(device.device)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_power_max_limit", device.tags, device.meta, map[string]interface{}{"value": float64(pwr_limit) / 1000}, time.Now())
if err == nil {
y.AddMeta("unit", "watts")
output <- y
}
}
}
return nil
}
func readEncUtilization(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
isMig, ret := nvml.DeviceIsMigDeviceHandle(device.device)
if ret != nvml.SUCCESS {
err := errors.New(nvml.ErrorString(ret))
return err
}
if !isMig {
return nil
}
if !device.excludeMetrics["nv_encoder_util"] {
// Retrieves the current utilization and sampling size in microseconds for the Encoder
//
// For Kepler or newer fully supported devices.
//
// Note: On MIG-enabled GPUs, querying encoder utilization is not currently supported.
enc_util, _, ret := nvml.DeviceGetEncoderUtilization(device.device)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_encoder_util", device.tags, device.meta, map[string]interface{}{"value": float64(enc_util)}, time.Now())
if err == nil {
y.AddMeta("unit", "%")
output <- y
}
}
}
return nil
}
func readDecUtilization(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
isMig, ret := nvml.DeviceIsMigDeviceHandle(device.device)
if ret != nvml.SUCCESS {
err := errors.New(nvml.ErrorString(ret))
return err
}
if !isMig {
return nil
}
if !device.excludeMetrics["nv_decoder_util"] {
// Retrieves the current utilization and sampling size in microseconds for the Encoder
//
// For Kepler or newer fully supported devices.
//
// Note: On MIG-enabled GPUs, querying encoder utilization is not currently supported.
dec_util, _, ret := nvml.DeviceGetDecoderUtilization(device.device)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_decoder_util", device.tags, device.meta, map[string]interface{}{"value": float64(dec_util)}, time.Now())
if err == nil {
y.AddMeta("unit", "%")
output <- y
}
}
}
return nil
}
func readRemappedRows(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_remapped_rows_corrected"] ||
!device.excludeMetrics["nv_remapped_rows_uncorrected"] ||
!device.excludeMetrics["nv_remapped_rows_pending"] ||
!device.excludeMetrics["nv_remapped_rows_failure"] {
// Get number of remapped rows. The number of rows reported will be based on the cause of the remapping.
// isPending indicates whether or not there are pending remappings.
// A reset will be required to actually remap the row.
// failureOccurred will be set if a row remapping ever failed in the past.
// A pending remapping won't affect future work on the GPU since error-containment and dynamic page blacklisting will take care of that.
//
// For Ampere or newer fully supported devices.
//
// Note: On MIG-enabled GPUs with active instances, querying the number of remapped rows is not supported
corrected, uncorrected, pending, failure, ret := nvml.DeviceGetRemappedRows(device.device)
if ret == nvml.SUCCESS {
if !device.excludeMetrics["nv_remapped_rows_corrected"] {
y, err := lp.New("nv_remapped_rows_corrected", device.tags, device.meta, map[string]interface{}{"value": float64(corrected)}, time.Now())
if err == nil {
output <- y
}
}
if !device.excludeMetrics["nv_remapped_rows_uncorrected"] {
y, err := lp.New("nv_remapped_rows_corrected", device.tags, device.meta, map[string]interface{}{"value": float64(uncorrected)}, time.Now())
if err == nil {
output <- y
}
}
if !device.excludeMetrics["nv_remapped_rows_pending"] {
var p int = 0
if pending {
p = 1
}
y, err := lp.New("nv_remapped_rows_pending", device.tags, device.meta, map[string]interface{}{"value": p}, time.Now())
if err == nil {
output <- y
}
}
if !device.excludeMetrics["nv_remapped_rows_failure"] {
var f int = 0
if failure {
f = 1
}
y, err := lp.New("nv_remapped_rows_failure", device.tags, device.meta, map[string]interface{}{"value": f}, time.Now())
if err == nil {
output <- y
}
}
}
}
return nil
}
func readProcessCounts(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
if !device.excludeMetrics["nv_compute_processes"] {
// Get information about processes with a compute context on a device
//
// For Fermi &tm; or newer fully supported devices.
//
// This function returns information only about compute running processes (e.g. CUDA application which have
// active context). Any graphics applications (e.g. using OpenGL, DirectX) won't be listed by this function.
//
// To query the current number of running compute processes, call this function with *infoCount = 0. The
// return code will be NVML_ERROR_INSUFFICIENT_SIZE, or NVML_SUCCESS if none are running. For this call
// \a infos is allowed to be NULL.
//
// The usedGpuMemory field returned is all of the memory used by the application.
//
// Keep in mind that information returned by this call is dynamic and the number of elements might change in
// time. Allocate more space for \a infos table in case new compute processes are spawned.
//
// @note In MIG mode, if device handle is provided, the API returns aggregate information, only if
// the caller has appropriate privileges. Per-instance information can be queried by using
// specific MIG device handles.
// Querying per-instance information using MIG device handles is not supported if the device is in vGPU Host virtualization mode.
procList, ret := nvml.DeviceGetComputeRunningProcesses(device.device)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_compute_processes", device.tags, device.meta, map[string]interface{}{"value": len(procList)}, time.Now())
if err == nil {
output <- y
}
}
}
if !device.excludeMetrics["nv_graphics_processes"] {
// Get information about processes with a graphics context on a device
//
// For Kepler &tm; or newer fully supported devices.
//
// This function returns information only about graphics based processes
// (eg. applications using OpenGL, DirectX)
//
// To query the current number of running graphics processes, call this function with *infoCount = 0. The
// return code will be NVML_ERROR_INSUFFICIENT_SIZE, or NVML_SUCCESS if none are running. For this call
// \a infos is allowed to be NULL.
//
// The usedGpuMemory field returned is all of the memory used by the application.
//
// Keep in mind that information returned by this call is dynamic and the number of elements might change in
// time. Allocate more space for \a infos table in case new graphics processes are spawned.
//
// @note In MIG mode, if device handle is provided, the API returns aggregate information, only if
// the caller has appropriate privileges. Per-instance information can be queried by using
// specific MIG device handles.
// Querying per-instance information using MIG device handles is not supported if the device is in vGPU Host virtualization mode.
procList, ret := nvml.DeviceGetGraphicsRunningProcesses(device.device)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_graphics_processes", device.tags, device.meta, map[string]interface{}{"value": len(procList)}, time.Now())
if err == nil {
output <- y
}
}
}
// if !device.excludeMetrics["nv_mps_compute_processes"] {
// // Get information about processes with a MPS compute context on a device
// //
// // For Volta &tm; or newer fully supported devices.
// //
// // This function returns information only about compute running processes (e.g. CUDA application which have
// // active context) utilizing MPS. Any graphics applications (e.g. using OpenGL, DirectX) won't be listed by
// // this function.
// //
// // To query the current number of running compute processes, call this function with *infoCount = 0. The
// // return code will be NVML_ERROR_INSUFFICIENT_SIZE, or NVML_SUCCESS if none are running. For this call
// // \a infos is allowed to be NULL.
// //
// // The usedGpuMemory field returned is all of the memory used by the application.
// //
// // Keep in mind that information returned by this call is dynamic and the number of elements might change in
// // time. Allocate more space for \a infos table in case new compute processes are spawned.
// //
// // @note In MIG mode, if device handle is provided, the API returns aggregate information, only if
// // the caller has appropriate privileges. Per-instance information can be queried by using
// // specific MIG device handles.
// // Querying per-instance information using MIG device handles is not supported if the device is in vGPU Host virtualization mode.
// procList, ret := nvml.DeviceGetMPSComputeRunningProcesses(device.device)
// if ret == nvml.SUCCESS {
// y, err := lp.New("nv_mps_compute_processes", device.tags, device.meta, map[string]interface{}{"value": len(procList)}, time.Now())
// if err == nil {
// output <- y
// }
// }
// }
return nil
}
func readViolationStats(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
var violTime nvml.ViolationTime
var ret nvml.Return
// Gets the duration of time during which the device was throttled (lower than requested clocks) due to power
// or thermal constraints.
//
// The method is important to users who are tying to understand if their GPUs throttle at any point during their applications. The
// difference in violation times at two different reference times gives the indication of GPU throttling event.
//
// Violation for thermal capping is not supported at this time.
//
// For Kepler or newer fully supported devices.
if !device.excludeMetrics["nv_violation_power"] {
// How long did power violations cause the GPU to be below application clocks
violTime, ret = nvml.DeviceGetViolationStatus(device.device, nvml.PERF_POLICY_POWER)
if ret == nvml.SUCCESS {
t := float64(violTime.ViolationTime) * 1e-9
y, err := lp.New("nv_violation_power", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "sec")
output <- y
}
}
}
if !device.excludeMetrics["nv_violation_thermal"] {
// How long did thermal violations cause the GPU to be below application clocks
violTime, ret = nvml.DeviceGetViolationStatus(device.device, nvml.PERF_POLICY_THERMAL)
if ret == nvml.SUCCESS {
t := float64(violTime.ViolationTime) * 1e-9
y, err := lp.New("nv_violation_thermal", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "sec")
output <- y
}
}
}
if !device.excludeMetrics["nv_violation_sync_boost"] {
// How long did sync boost cause the GPU to be below application clocks
violTime, ret = nvml.DeviceGetViolationStatus(device.device, nvml.PERF_POLICY_SYNC_BOOST)
if ret == nvml.SUCCESS {
t := float64(violTime.ViolationTime) * 1e-9
y, err := lp.New("nv_violation_sync_boost", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "sec")
output <- y
}
}
}
if !device.excludeMetrics["nv_violation_board_limit"] {
// How long did the board limit cause the GPU to be below application clocks
violTime, ret = nvml.DeviceGetViolationStatus(device.device, nvml.PERF_POLICY_BOARD_LIMIT)
if ret == nvml.SUCCESS {
t := float64(violTime.ViolationTime) * 1e-9
y, err := lp.New("nv_violation_board_limit", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "sec")
output <- y
}
}
}
if !device.excludeMetrics["nv_violation_low_util"] {
// How long did low utilization cause the GPU to be below application clocks
violTime, ret = nvml.DeviceGetViolationStatus(device.device, nvml.PERF_POLICY_LOW_UTILIZATION)
if ret == nvml.SUCCESS {
t := float64(violTime.ViolationTime) * 1e-9
y, err := lp.New("nv_violation_low_util", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "sec")
output <- y
}
}
}
if !device.excludeMetrics["nv_violation_reliability"] {
// How long did the board reliability limit cause the GPU to be below application clocks
violTime, ret = nvml.DeviceGetViolationStatus(device.device, nvml.PERF_POLICY_RELIABILITY)
if ret == nvml.SUCCESS {
t := float64(violTime.ViolationTime) * 1e-9
y, err := lp.New("nv_violation_reliability", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "sec")
output <- y
}
}
}
if !device.excludeMetrics["nv_violation_below_app_clock"] {
// Total time the GPU was held below application clocks by any limiter (all of above)
violTime, ret = nvml.DeviceGetViolationStatus(device.device, nvml.PERF_POLICY_TOTAL_APP_CLOCKS)
if ret == nvml.SUCCESS {
t := float64(violTime.ViolationTime) * 1e-9
y, err := lp.New("nv_violation_below_app_clock", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "sec")
output <- y
}
}
}
if !device.excludeMetrics["nv_violation_below_base_clock"] {
// Total time the GPU was held below base clocks
violTime, ret = nvml.DeviceGetViolationStatus(device.device, nvml.PERF_POLICY_TOTAL_BASE_CLOCKS)
if ret == nvml.SUCCESS {
t := float64(violTime.ViolationTime) * 1e-9
y, err := lp.New("nv_violation_below_base_clock", device.tags, device.meta, map[string]interface{}{"value": t}, time.Now())
if err == nil {
y.AddMeta("unit", "sec")
output <- y
}
}
}
return nil
}
func readNVLinkStats(device NvidiaCollectorDevice, output chan lp.CCMetric) error {
// Retrieves the specified error counter value
// Please refer to \a nvmlNvLinkErrorCounter_t for error counters that are available
//
// For Pascal &tm; or newer fully supported devices.
for i := 0; i < nvml.NVLINK_MAX_LINKS; i++ {
state, ret := nvml.DeviceGetNvLinkState(device.device, i)
if ret == nvml.SUCCESS {
if state == nvml.FEATURE_ENABLED {
if !device.excludeMetrics["nv_nvlink_crc_errors"] {
// Data link receive data CRC error counter
count, ret := nvml.DeviceGetNvLinkErrorCounter(device.device, i, nvml.NVLINK_ERROR_DL_CRC_DATA)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_nvlink_crc_errors", device.tags, device.meta, map[string]interface{}{"value": count}, time.Now())
if err == nil {
y.AddTag("stype", "nvlink")
y.AddTag("stype-id", fmt.Sprintf("%d", i))
output <- y
}
}
}
if !device.excludeMetrics["nv_nvlink_ecc_errors"] {
// Data link receive data ECC error counter
count, ret := nvml.DeviceGetNvLinkErrorCounter(device.device, i, nvml.NVLINK_ERROR_DL_ECC_DATA)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_nvlink_ecc_errors", device.tags, device.meta, map[string]interface{}{"value": count}, time.Now())
if err == nil {
y.AddTag("stype", "nvlink")
y.AddTag("stype-id", fmt.Sprintf("%d", i))
output <- y
}
}
}
if !device.excludeMetrics["nv_nvlink_replay_errors"] {
// Data link transmit replay error counter
count, ret := nvml.DeviceGetNvLinkErrorCounter(device.device, i, nvml.NVLINK_ERROR_DL_REPLAY)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_nvlink_replay_errors", device.tags, device.meta, map[string]interface{}{"value": count}, time.Now())
if err == nil {
y.AddTag("stype", "nvlink")
y.AddTag("stype-id", fmt.Sprintf("%d", i))
output <- y
}
}
}
if !device.excludeMetrics["nv_nvlink_recovery_errors"] {
// Data link transmit recovery error counter
count, ret := nvml.DeviceGetNvLinkErrorCounter(device.device, i, nvml.NVLINK_ERROR_DL_RECOVERY)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_nvlink_recovery_errors", device.tags, device.meta, map[string]interface{}{"value": count}, time.Now())
if err == nil {
y.AddTag("stype", "nvlink")
y.AddTag("stype-id", fmt.Sprintf("%d", i))
output <- y
}
}
}
if !device.excludeMetrics["nv_nvlink_crc_flit_errors"] {
// Data link receive flow control digit CRC error counter
count, ret := nvml.DeviceGetNvLinkErrorCounter(device.device, i, nvml.NVLINK_ERROR_DL_CRC_FLIT)
if ret == nvml.SUCCESS {
y, err := lp.New("nv_nvlink_crc_flit_errors", device.tags, device.meta, map[string]interface{}{"value": count}, time.Now())
if err == nil {
y.AddTag("stype", "nvlink")
y.AddTag("stype-id", fmt.Sprintf("%d", i))
output <- y
}
}
}
}
}
}
return nil
}
func (m *NvidiaCollector) Read(interval time.Duration, output chan lp.CCMetric) {
var err error
if !m.init {
return
}
readAll := func(device NvidiaCollectorDevice, output chan lp.CCMetric) {
name, ret := nvml.DeviceGetName(device.device)
if ret != nvml.SUCCESS {
name = "NoName"
}
err = readMemoryInfo(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readMemoryInfo for device", name, "failed")
}
err = readUtilization(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readUtilization for device", name, "failed")
}
err = readTemp(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readTemp for device", name, "failed")
}
err = readFan(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readFan for device", name, "failed")
}
err = readEccMode(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readEccMode for device", name, "failed")
}
err = readPerfState(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readPerfState for device", name, "failed")
}
err = readPowerUsage(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readPowerUsage for device", name, "failed")
}
err = readClocks(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readClocks for device", name, "failed")
}
err = readMaxClocks(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readMaxClocks for device", name, "failed")
}
err = readEccErrors(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readEccErrors for device", name, "failed")
}
err = readPowerLimit(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readPowerLimit for device", name, "failed")
}
err = readEncUtilization(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readEncUtilization for device", name, "failed")
}
err = readDecUtilization(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readDecUtilization for device", name, "failed")
}
err = readRemappedRows(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readRemappedRows for device", name, "failed")
}
err = readBarMemoryInfo(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readBarMemoryInfo for device", name, "failed")
}
err = readProcessCounts(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readProcessCounts for device", name, "failed")
}
err = readViolationStats(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readViolationStats for device", name, "failed")
}
err = readNVLinkStats(device, output)
if err != nil {
cclog.ComponentDebug(m.name, "readNVLinkStats for device", name, "failed")
}
}
// Actual read loop over all attached Nvidia GPUs
for i := 0; i < m.num_gpus; i++ {
readAll(m.gpus[i], output)
// Iterate over all MIG devices if any
if m.config.ProcessMigDevices {
current, _, ret := nvml.DeviceGetMigMode(m.gpus[i].device)
if ret != nvml.SUCCESS {
continue
}
if current == nvml.DEVICE_MIG_DISABLE {
continue
}
maxMig, ret := nvml.DeviceGetMaxMigDeviceCount(m.gpus[i].device)
if ret != nvml.SUCCESS {
continue
}
if maxMig == 0 {
continue
}
cclog.ComponentDebug(m.name, "Reading MIG devices for GPU", i)
for j := 0; j < maxMig; j++ {
mdev, ret := nvml.DeviceGetMigDeviceHandleByIndex(m.gpus[i].device, j)
if ret != nvml.SUCCESS {
continue
}
excludeMetrics := make(map[string]bool)
for _, metric := range m.config.ExcludeMetrics {
excludeMetrics[metric] = true
}
migDevice := NvidiaCollectorDevice{
device: mdev,
tags: map[string]string{},
meta: map[string]string{},
excludeMetrics: excludeMetrics,
}
for k, v := range m.gpus[i].tags {
migDevice.tags[k] = v
}
m.gpus[i].tags["stype"] = "mig"
m.gpus[i].tags["stype-id"] = fmt.Sprintf("%d", j)
for k, v := range m.gpus[i].meta {
migDevice.meta[k] = v
}
if _, ok := migDevice.meta["uuid"]; ok {
uuid, ret := nvml.DeviceGetUUID(mdev)
if ret == nvml.SUCCESS {
migDevice.meta["uuid"] = uuid
}
}
readAll(migDevice, output)
}
}
}
}
func (m *NvidiaCollector) Close() {
if m.init {
nvml.Shutdown()
m.init = false
}
}