diff --git a/.azure-pipelines/steps/run.yml b/.azure-pipelines/steps/run.yml
index 49bac629e7285..4875e2c6754a4 100644
--- a/.azure-pipelines/steps/run.yml
+++ b/.azure-pipelines/steps/run.yml
@@ -11,6 +11,12 @@ steps:
 - checkout: self
   fetchDepth: 2
 
+# Spawn a background process to collect CPU usage statistics which we'll upload
+# at the end of the build. See the comments in the script here for more
+# information.
+- bash: python src/ci/cpu-usage-over-time.py &> cpu-usage.csv &
+  displayName: "Collect CPU-usage statistics in the background"
+
 - bash: printenv | sort
   displayName: Show environment variables
 
@@ -142,3 +148,13 @@ steps:
     AWS_SECRET_ACCESS_KEY: $(AWS_SECRET_ACCESS_KEY)
   condition: and(succeeded(), or(eq(variables.DEPLOY, '1'), eq(variables.DEPLOY_ALT, '1')))
   displayName: Upload artifacts
+
+# Upload CPU usage statistics that we've been gathering this whole time. Always
+# execute this step in case we want to inspect failed builds, but don't let
+# errors here ever fail the build since this is just informational.
+- bash: aws s3 cp --acl public-read cpu-usage.csv s3://$DEPLOY_BUCKET/rustc-builds/$BUILD_SOURCEVERSION/cpu-$SYSTEM_JOBNAME.csv
+  env:
+    AWS_SECRET_ACCESS_KEY: $(AWS_SECRET_ACCESS_KEY)
+  condition: contains(variables, 'AWS_SECRET_ACCESS_KEY')
+  continueOnError: true
+  displayName: Upload CPU usage statistics
diff --git a/src/ci/cpu-usage-over-time.py b/src/ci/cpu-usage-over-time.py
new file mode 100644
index 0000000000000..78427a6360a9f
--- /dev/null
+++ b/src/ci/cpu-usage-over-time.py
@@ -0,0 +1,175 @@
+#!/usr/bin/env python
+# ignore-tidy-linelength
+
+# This is a small script that we use on CI to collect CPU usage statistics of
+# our builders. By seeing graphs of CPU usage over time we hope to correlate
+# that with possible improvements to Rust's own build system, ideally diagnosing
+# that either builders are always fully using their CPU resources or they're
+# idle for long stretches of time.
+#
+# This script is relatively simple, but it's platform specific. Each platform
+# (OSX/Windows/Linux) has a different way of calculating the current state of
+# CPU at a point in time. We then compare two captured states to determine the
+# percentage of time spent in one state versus another. The state capturing is
+# all platform-specific but the loop at the bottom is the cross platform part
+# that executes everywhere.
+#
+# # Viewing statistics
+#
+# All builders will upload their CPU statistics as CSV files to our S3 buckets.
+# These URLS look like:
+#
+#   https://$bucket.s3.amazonaws.com/rustc-builds/$commit/cpu-$builder.csv
+#
+# for example
+#
+#   https://rust-lang-ci2.s3.amazonaws.com/rustc-builds/68baada19cd5340f05f0db15a3e16d6671609bcc/cpu-x86_64-apple.csv
+#
+# Each CSV file has two columns. The first is the timestamp of the measurement
+# and the second column is the % of idle cpu time in that time slice. Ideally
+# the second column is always zero.
+#
+# Once you've downloaded a file there's various ways to plot it and visualize
+# it. For command line usage you can use a script like so:
+#
+#      set timefmt '%Y-%m-%dT%H:%M:%S'
+#      set xdata time
+#      set ylabel "Idle CPU %"
+#      set xlabel "Time"
+#      set datafile sep ','
+#      set term png
+#      set output "printme.png"
+#      set grid
+#      builder = "i686-apple"
+#      plot "cpu-".builder.".csv" using 1:2 with lines title builder
+#
+# Executed as `gnuplot < ./foo.plot` it will generate a graph called
+# `printme.png` which you can then open up. If you know how to improve this
+# script or the viewing process that would be much appreciated :) (or even if
+# you know how to automate it!)
+
+import datetime
+import sys
+import time
+
+if sys.platform == 'linux2':
+    class State:
+        def __init__(self):
+            with open('/proc/stat', 'r') as file:
+                data = file.readline().split()
+            if data[0] != 'cpu':
+                raise Exception('did not start with "cpu"')
+            self.user = int(data[1])
+            self.nice = int(data[2])
+            self.system = int(data[3])
+            self.idle = int(data[4])
+            self.iowait = int(data[5])
+            self.irq = int(data[6])
+            self.softirq = int(data[7])
+            self.steal = int(data[8])
+            self.guest = int(data[9])
+            self.guest_nice = int(data[10])
+
+        def idle_since(self, prev):
+            user = self.user - prev.user
+            nice = self.nice - prev.nice
+            system = self.system - prev.system
+            idle = self.idle - prev.idle
+            iowait = self.iowait - prev.iowait
+            irq = self.irq - prev.irq
+            softirq = self.softirq - prev.softirq
+            steal = self.steal - prev.steal
+            guest = self.guest - prev.guest
+            guest_nice = self.guest_nice - prev.guest_nice
+            total = user + nice + system + idle + iowait + irq + softirq + steal + guest + guest_nice
+            return float(idle) / float(total) * 100
+
+elif sys.platform == 'win32':
+    from ctypes.wintypes import DWORD
+    from ctypes import Structure, windll, WinError, GetLastError, byref
+
+    class FILETIME(Structure):
+        _fields_ = [
+            ("dwLowDateTime", DWORD),
+            ("dwHighDateTime", DWORD),
+        ]
+
+    class State:
+        def __init__(self):
+            idle, kernel, user = FILETIME(), FILETIME(), FILETIME()
+
+            success = windll.kernel32.GetSystemTimes(
+                byref(idle),
+                byref(kernel),
+                byref(user),
+            )
+
+            assert success, WinError(GetLastError())[1]
+
+            self.idle = (idle.dwHighDateTime << 32) | idle.dwLowDateTime
+            self.kernel = (kernel.dwHighDateTime << 32) | kernel.dwLowDateTime
+            self.user = (user.dwHighDateTime << 32) | user.dwLowDateTime
+
+        def idle_since(self, prev):
+            idle = self.idle - prev.idle
+            user = self.user - prev.user
+            kernel = self.kernel - prev.kernel
+            return float(idle) / float(user + kernel) * 100
+
+elif sys.platform == 'darwin':
+    from ctypes import *
+    libc = cdll.LoadLibrary('/usr/lib/libc.dylib')
+
+    PROESSOR_CPU_LOAD_INFO = c_int(2)
+    CPU_STATE_USER = 0
+    CPU_STATE_SYSTEM = 1
+    CPU_STATE_IDLE = 2
+    CPU_STATE_NICE = 3
+    c_int_p = POINTER(c_int)
+
+    class State:
+        def __init__(self):
+            num_cpus_u = c_uint(0)
+            cpu_info = c_int_p()
+            cpu_info_cnt = c_int(0)
+            err = libc.host_processor_info(
+                libc.mach_host_self(),
+                PROESSOR_CPU_LOAD_INFO,
+                byref(num_cpus_u),
+                byref(cpu_info),
+                byref(cpu_info_cnt),
+            )
+            assert err == 0
+            self.user = 0
+            self.system = 0
+            self.idle = 0
+            self.nice = 0
+            cur = 0
+            while cur < cpu_info_cnt.value:
+                self.user += cpu_info[cur + CPU_STATE_USER]
+                self.system += cpu_info[cur + CPU_STATE_SYSTEM]
+                self.idle += cpu_info[cur + CPU_STATE_IDLE]
+                self.nice += cpu_info[cur + CPU_STATE_NICE]
+                cur += num_cpus_u.value
+
+        def idle_since(self, prev):
+            user = self.user - prev.user
+            system = self.system - prev.system
+            idle = self.idle - prev.idle
+            nice = self.nice - prev.nice
+            return float(idle) / float(user + system + idle + nice) * 100.0
+
+else:
+    print('unknown platform', sys.platform)
+    sys.exit(1)
+
+cur_state = State();
+print("Time,Idle")
+while True:
+    time.sleep(1);
+    next_state = State();
+    now = datetime.datetime.utcnow().isoformat()
+    idle = next_state.idle_since(cur_state)
+    print("%s,%s" % (now, idle))
+    sys.stdout.flush()
+    cur_state = next_state