MySQL: Slow log; append CPU usage
While I don't know of any tool that provides this information specifically, Percona Server does have a setting (log_slow_verbosity=full) that enables extended statisitics in the slow log along with microsecond granularity.  The data does not explicitly include CPU utilization, but it does provide a number of additional statistics about the internals of MySQL and InnoDB that may help you determine whether or not the problem is CPU related or a result of poor optimization.

This is an example of the additional information in the slow log:

# Time: 120114  6:34:33
# User@Host: user[user] @  [10.10.10.10]
# Thread_id: 28313080  Schema: mydb  Last_errno: 0  Killed: 0
# Query_time: 0.588882  Lock_time: 0.000068  Rows_sent: 3  Rows_examined: 183839  Rows_affected: 0  Rows_read: 100
# Bytes_sent: 121  Tmp_tables: 0  Tmp_disk_tables: 0  Tmp_table_sizes: 0
# InnoDB_trx_id: 9903E4DB1
# QC_Hit: No  Full_scan: No  Full_join: No  Tmp_table: No  Tmp_table_on_disk: No
# Filesort: No  Filesort_on_disk: No  Merge_passes: 0
#   InnoDB_IO_r_ops: 0  InnoDB_IO_r_bytes: 0  InnoDB_IO_r_wait: 0.000000
#   InnoDB_rec_lock_wait: 0.000000  InnoDB_queue_wait: 0.000000
#   InnoDB_pages_distinct: 11359


Also, pt-query-digest from the Percona Toolkit understands this additional information and will summarize the slow query log for you and produce output, per query, that looks like this:

# Query 1: 0.09 QPS, 0.07x concurrency, ID 0x20112A1CCDBDBA4E at byte 4337645
# Scores: Apdex = 1.00 [1.0], V/M = 0.01
# Query_time sparkline: |     ^  |
# Time range: 2012-01-14 06:34:33 to 08:51:57
# Attribute    pct   total     min     max     avg     95%  stddev  median
# ============ === ======= ======= ======= ======= ======= ======= =======
# Count         63     775
# Exec time     59    539s   522ms   855ms   695ms   816ms    87ms   640ms
# Lock time     18    60ms    43us   498us    76us   103us    36us    66us
# Rows sent      1   3.15k       0      38    4.17   12.54    4.90    2.90
# Rows examine  22 135.93M 179.38k 179.90k 179.60k 174.27k       0 174.27k
# Rows affecte   0       0       0       0       0       0       0       0
# Rows read      0   3.43k       0     118    4.53   20.43   11.93    0.99
# Bytes sent     1 103.07k      82     576  136.18  246.02   63.85  118.34
# Merge passes   0       0       0       0       0       0       0       0
# Tmp tables     0       0       0       0       0       0       0       0
# Tmp disk tbl   0       0       0       0       0       0       0       0
# Tmp tbl size   0       0       0       0       0       0       0       0
# Query size     5 222.51k     294     294     294     294       0     294
# InnoDB:
# IO r bytes     0       0       0       0       0       0       0       0
# IO r ops       0       0       0       0       0       0       0       0
# IO r wait      0       0       0       0       0       0       0       0
# pages distin  63   8.44M  10.66k  12.72k  11.15k  11.34k  357.67  10.80k
# queue wait     0       0       0       0       0       0       0       0
# rec lock wai   0       0       0       0       0       0       0       0
# String:
# Databases    mydb
# Hosts
# InnoDB trxID 9903E4DB1 (1/0%), 9903E4E09 (1/0%)... 773 more
# Last errno   0
# Users        user
# Query_time distribution
#   1us
#  10us
# 100us
#   1ms
#  10ms
# 100ms  ################################################################
#    1s
#  10s+
# Tables
#    SHOW TABLE STATUS FROM `mydb` LIKE 'table'\G
#    SHOW CREATE TABLE `mydb`.`table`\G
# EXPLAIN /*!50100 PARTITIONS*/


...followed by the query itself.  

I have blogged a bit about using pt-query-digest and other tools for finding performance bottlenecks (and promised more blogging, but haven't gotten around to it yet).

Another tool which may help you is pt-stalk and pt-collect.  This will allow you to set a threshold based on a running server variable, such as when the number of concurrently running threads jumps up (Threads_running) and then collect a wealth of data about the running system, including cpu, I/O, MySQL statistics, the running processlist, etc to give you the ability to perform a thorough post-mortem.  

The additional statistics are only available in Percona Server, but these tools will all work with MySQL GA.