Update: MapD rebranded as OmniSci in 2018.

For most of 2016 I've been using a dataset of 1.1 billion taxi journeys made in New York City over the course of six years to benchmark various Big Data solutions. Though these aren't apples-for-apples comparisons, I've benchmarked BigQuery, Elasticsearch, Presto on EMR and Dataproc, PostgreSQL and Redshift using the same dataset.

So far all of the benchmarks have a common theme in that they are running on Intel CPUs. Probably the most dramatic difference between using GPUs versus a run-of-the-mill CPU is that memory read speeds should be able to hit 300 GB/s due to a much wider bus whereas with CPUs you're looking at closer to 20 GB/s. This, coupled with a number of other architectural differences has resulted in some of my benchmark queries running 55x quicker than the fastest benchmarks I've performed in the past.

A Supercomputer Up & Running MapD have been kind enough to grant me access to a machine that I'll use to benchmark their GPU-based database software with the 1.1 billion taxi trips dataset. The machine I'll be using would have been one of the world's 20 fastest computers 10 years ago. It has 8 x Nvidia Telsa K80s, each with 2 GPUs per card. The K80 has 2.91 teraflops of double-precision performance and 8.73 teraflops single-precision performance giving me 23.28 and 69.84 teraflops of performance respectively. The following is the output from Nvidia's system management interface showing various diagnostics of the GPU cluster. $ nvidia-smi +------------------------------------------------------+ | NVIDIA-SMI 352.93 Driver Version: 352.93 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla K80 On | 0000:06:00.0 Off | Off | | N/A 48C P0 67W / 149W | 2748MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 1 Tesla K80 On | 0000:07:00.0 Off | Off | | N/A 36C P0 78W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 2 Tesla K80 On | 0000:0A:00.0 Off | Off | | N/A 47C P0 66W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 3 Tesla K80 On | 0000:0B:00.0 Off | Off | | N/A 35C P0 77W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 4 Tesla K80 On | 0000:10:00.0 Off | Off | | N/A 44C P0 64W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 5 Tesla K80 On | 0000:11:00.0 Off | Off | | N/A 33C P0 76W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 6 Tesla K80 On | 0000:14:00.0 Off | Off | | N/A 47C P0 64W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 7 Tesla K80 On | 0000:15:00.0 Off | Off | | N/A 36C P0 74W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 8 Tesla K80 On | 0000:86:00.0 Off | Off | | N/A 43C P0 61W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 9 Tesla K80 On | 0000:87:00.0 Off | Off | | N/A 34C P0 77W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 10 Tesla K80 On | 0000:8A:00.0 Off | Off | | N/A 47C P0 66W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 11 Tesla K80 On | 0000:8B:00.0 Off | Off | | N/A 34C P0 78W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 12 Tesla K80 On | 0000:90:00.0 Off | Off | | N/A 44C P0 64W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 13 Tesla K80 On | 0000:91:00.0 Off | Off | | N/A 36C P0 77W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 14 Tesla K80 On | 0000:94:00.0 Off | Off | | N/A 45C P0 65W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 15 Tesla K80 On | 0000:95:00.0 Off | Off | | N/A 36C P0 76W / 149W | 2714MiB / 12287MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2666MiB | | 0 10718 G /usr/bin/Xorg 16MiB | | 1 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 1 10718 G /usr/bin/Xorg 16MiB | | 2 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 2 10718 G /usr/bin/Xorg 16MiB | | 3 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 3 10718 G /usr/bin/Xorg 16MiB | | 4 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 4 10718 G /usr/bin/Xorg 16MiB | | 5 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 5 10718 G /usr/bin/Xorg 16MiB | | 6 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 6 10718 G /usr/bin/Xorg 16MiB | | 7 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 7 10718 G /usr/bin/Xorg 16MiB | | 8 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 8 10718 G /usr/bin/Xorg 16MiB | | 9 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 9 10718 G /usr/bin/Xorg 16MiB | | 10 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 10 10718 G /usr/bin/Xorg 16MiB | | 11 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 11 10718 G /usr/bin/Xorg 16MiB | | 12 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 12 10718 G /usr/bin/Xorg 16MiB | | 13 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 13 10718 G /usr/bin/Xorg 16MiB | | 14 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 14 10718 G /usr/bin/Xorg 16MiB | | 15 7542 C+G /raidStorage/prod/mapd/bin/mapd_server 2632MiB | | 15 10718 G /usr/bin/Xorg 16MiB | +-----------------------------------------------------------------------------+ The machine is running CentOS 7.2.1511 on a 16-core, 32-thread Intel Xeon E5-2667 v3 clocked at 3.2 GHz with 792 GB of RAM. I'll be using a RAID array to store both the raw CSV files and MapD's internal columnar-based files it uses to represent the dataset. The RAID array is comprised of 4 x Samsung EVO 2 TB SSDs in a RAID 10 configuration with a LSI Logic / Symbios Logic MegaRAID SAS2108 RAID bus controller making for a total of 4 TB of usable storage. This configuration should see sequential read speeds of up to 500 MB/s.

Loading 1.1 Billion Trips into MapD To start, I'll download the 104 GB of CSV data I created in my Billion Taxi Rides in Redshift blog post. This data sits in 56 GZIP files and decompresses into around 500 GB of raw CSV data. $ cd /raidStorage/mark/ $ vi urls.txt https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xaa.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xab.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xac.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xad.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xae.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xaf.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xag.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xah.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xai.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xaj.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xak.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xal.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xam.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xan.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xao.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xap.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xaq.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xar.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xas.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xat.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xau.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xav.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xaw.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xax.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xay.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xaz.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xba.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbb.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbc.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbd.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbe.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbf.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbg.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbh.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbi.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbj.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbk.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbl.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbm.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbn.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbo.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbp.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbq.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbr.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbs.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbt.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbu.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbv.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbw.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbx.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xby.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xbz.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xca.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xcb.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xcc.csv.gz https://s3-eu-west-1.amazonaws.com/<s3_bucket>/csv/trips_xcd.csv.gz $ cat urls.txt | \ xargs -n 1 -P 6 \ wget I'll then create a schema for my trips table. The SQL looks very much like what you'd use with most RDBMS offerings. Two items which will stand out is the dictionary encoding on string columns with low cardinality and a fragment size parameter. The fragment size helps split the 1.1 billion records amongst all of the 8 K80 cards so they can work in parallel with roughly equal workloads. $ vi create_trips_table.sql CREATE TABLE trips ( trip_id INTEGER , vendor_id VARCHAR ( 3 ) ENCODING DICT , pickup_datetime TIMESTAMP , dropoff_datetime TIMESTAMP , store_and_fwd_flag VARCHAR ( 1 ) ENCODING DICT , rate_code_id SMALLINT , pickup_longitude DECIMAL ( 14 , 2 ), pickup_latitude DECIMAL ( 14 , 2 ), dropoff_longitude DECIMAL ( 14 , 2 ), dropoff_latitude DECIMAL ( 14 , 2 ), passenger_count SMALLINT , trip_distance DECIMAL ( 14 , 2 ), fare_amount DECIMAL ( 14 , 2 ), extra DECIMAL ( 14 , 2 ), mta_tax DECIMAL ( 14 , 2 ), tip_amount DECIMAL ( 14 , 2 ), tolls_amount DECIMAL ( 14 , 2 ), ehail_fee DECIMAL ( 14 , 2 ), improvement_surcharge DECIMAL ( 14 , 2 ), total_amount DECIMAL ( 14 , 2 ), payment_type VARCHAR ( 3 ) ENCODING DICT , trip_type SMALLINT , pickup VARCHAR ( 50 ) ENCODING DICT , dropoff VARCHAR ( 50 ) ENCODING DICT , cab_type VARCHAR ( 6 ) ENCODING DICT , precipitation SMALLINT , snow_depth SMALLINT , snowfall SMALLINT , max_temperature SMALLINT , min_temperature SMALLINT , average_wind_speed SMALLINT , pickup_nyct2010_gid SMALLINT , pickup_ctlabel VARCHAR ( 10 ) ENCODING DICT , pickup_borocode SMALLINT , pickup_boroname VARCHAR ( 13 ) ENCODING DICT , pickup_ct2010 VARCHAR ( 6 ) ENCODING DICT , pickup_boroct2010 VARCHAR ( 7 ) ENCODING DICT , pickup_cdeligibil VARCHAR ( 1 ) ENCODING DICT , pickup_ntacode VARCHAR ( 4 ) ENCODING DICT , pickup_ntaname VARCHAR ( 56 ) ENCODING DICT , pickup_puma VARCHAR ( 4 ) ENCODING DICT , dropoff_nyct2010_gid SMALLINT , dropoff_ctlabel VARCHAR ( 10 ) ENCODING DICT , dropoff_borocode SMALLINT , dropoff_boroname VARCHAR ( 13 ) ENCODING DICT , dropoff_ct2010 VARCHAR ( 6 ) ENCODING DICT , dropoff_boroct2010 VARCHAR ( 7 ) ENCODING DICT , dropoff_cdeligibil VARCHAR ( 1 ) ENCODING DICT , dropoff_ntacode VARCHAR ( 4 ) ENCODING DICT , dropoff_ntaname VARCHAR ( 56 ) ENCODING DICT , dropoff_puma VARCHAR ( 4 ) ENCODING DICT ) with ( fragment_size = 35000000 ); I'll create environment variables with my credentials for MapD. $ read MAPD_USERNAME $ read MAPD_PASSWORD $ export MAPD_USERNAME $ export MAPD_PASSWORD The following will create the table schema using the mapdql cli tool. $ mapdql mapd \ -u $MAPD_USERNAME \ -p $MAPD_PASSWORD \ < create_trips_table.sql I'll then check that the table has been created: $ echo "\t" | mapdql mapd -u $MAPD_USERNAME -p $MAPD_PASSWORD User mapd connected to database mapd trips User mapd disconnected from database mapd MapD doesn't support loading CSV data from GZIP files at this time so I'll decompress the CSV files before loading them. $ gunzip trips_x*.csv.gz With the table and files in place I'll load the 500 GB of CSV data into MapD. $ for filename in *.csv ; do echo "COPY trips FROM '/raidStorage/mark/ $filename ' WITH (header='false');" | \ mapdql \ mapd \ -u $MAPD_USERNAME \ -p $MAPD_PASSWORD done The above completed in 51 minutes and 47 seconds.