My presentation slides at PGconf.SV 2016 PL/CUDA - Fusion of HPC Grade Power with In-Database Analytics http://www.pgconfsv.com/

1. PL/CUDA
~Fusion of HPC Grade Power with In-Database Analytics~
The PG-Strom Project / NEC OSS Promotion Center
KaiGai Kohei <kaigai@ak.jp.nec.com>

2. The PG-Strom Project
about myself...
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics2
▌KaiGai Kohei
 tw: @kkaigai
 https://github.com/kaigai
▌PostgreSQL
 SELinux, FDW, CustomScan, ...
▌PG-Strom
 GPU acceleration for PostgreSQL
▌Works
 NEC OSS Promotion Center
 Development of the software and
its business opportunity

3. The PG-Strom Project
PG-Strom Overview (1/2) – Architecture
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics3
Application
Storage
Query
Optimizer
Query
Executor
PG-Strom
Extension
SQL Parser
Storage Manager
GPU
No Storage
Changes
No Query
Changes
 Features
• automatic GPU code generation
from the supplied SQL
• asynchronous & massive
parallel execution on GPUs
• WHERE-clause, JOIN, GROUP
BY, and projection are
supported
 Advantages
• transparent acceleration by the
power of thousands cores
• Low cost solution for analytic
processing

4. The PG-Strom Project
Characteristics of GPU (Graphic Processor Unit)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics4
GPU CPU
Model
NVIDIA
Tesla P100
Intel Xeon
E5-2699v4
Architecture Pascal Broadwell
Launch Q2-2016 Q1-2016
# of transistors 15billion 7.2billion
# of cores
3584
(simple)
22
(functional)
core clock
1.126GHz
~1.303GHz
2.20GHz
~3.60GHz
Perk FFLOPS
(FP32)
9.3 TFLOPS
1.2 TFLOPS
(with AVX2)
DRAM Size 16GB (HBM2) max 1.5TB (DDR4)
Memory Band 732GB/s 76.8GB/s
Power
Consumption
250W 145W

5. The PG-Strom Project
PG-Strom Overview (2/2) – GPU binary generation on the fly
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics
QUERY: SELECT cat, count(*), avg(x) FROM t0
WHERE x between y and y + 20.0 GROUP BY cat;
:
STATIC_FUNCTION(bool)
gpupreagg_qual_eval(kern_context *kcxt,
kern_data_store *kds,
size_t kds_index)
{
pg_float8_t KPARAM_1 = pg_float8_param(kcxt,1);
pg_float8_t KVAR_3 = pg_float8_vref(kds,kcxt,2,kds_index);
pg_float8_t KVAR_4 = pg_float8_vref(kds,kcxt,3,kds_index);
return EVAL((pgfn_float8ge(kcxt, KVAR_3, KVAR_4) &&
pgfn_float8le(kcxt, KVAR_3,
pgfn_float8pl(kcxt, KVAR_4, KPARAM_1))));
} :
E.g) Transform of arithmetic operations
in the WHERE-clause to CUDA programs
Reference to input data
SQL expression in CUDA source code
Run-time
Compiler
(nvrtc)
Just-in-time
Compile
Parallel
Execution
5

6. The PG-Strom Project
GPU accelerates SQL performance
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics6
▌Test Query:
SELECT cat, count(*), avg(x)
FROM t0 NATURAL JOIN t1 [NATURAL JOIN t2 ...]
GROUP BY cat;
 t0 contains 100M rows, t1...t8 contains 100K rows (like a start schema)
40.44
62.79
79.82
100.50
119.51
144.55
201.12
248.95
9.96 9.93 9.96 9.99 10.02 10.03 9.98 10.00
0
50
100
150
200
250
300
2 3 4 5 6 7 8 9
QueryResponseTime[sec]
Number of joined tables
PG-Strom microbenchmark with JOIN/GROUP BY
PostgreSQL v9.5 PG-Strom v1.0
CPU: Xeon E5-2670v3
GPU: GTX1080
RAM: 384GB
OS: CentOS 7.2
DB: PostgreSQL 9.5 +
PG-Strom v1.0

7. The PG-Strom Project
Feedbacks from users during v1.0 development
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics7
Application
Storage
Query
Optimizer
Query
Executor
PG-Strom
Extension
SQL Parser
Storage Manager
GPU
Heavy computing
intensive workloads
• In-database Analytics
• Scientific R&D, Marketing, ...
 by PL/CUDA + Matrix-Array
Heavy I/O
intensive workloads
• Generic large OLAP
• ETL, Reporting, ...
 by SSD-to-GPU P2P DMA

8. The PG-Strom ProjectPGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics8
Introduction of PL/CUDA

9. The PG-Strom Project
Our Failure (1/3) – Write an algorithm logic in SQL
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics9
Apr-2016

10. The PG-Strom Project
Our Failure (2/3) – Performance benefit (?)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics10
Apr-2016

11. The PG-Strom Project
Our Failure (3/3) – Problems
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics11
▌Problem.1 – Who writes algorithms in SQL?
 Majority of mathematical algorithms are developed based on
the manner of procedural programming language.
 Although users don’t need to write algorithm logics in CUDA,
they also have to write up the algorithm logic using SQL puzzle.
▌Problem.2 – Performance Benefit
 Yeah, PG-Strom is much faster than PostgreSQL to execute the core
logic of min-max method. It is an excellent result but nobody knows
how many people run the algorithm on PostgreSQL.
 Performance of GpuProjection is almost equivalent to the CPU version
of implementation which is designed to a particular problem. Why?
 Inefficient code due to SQL compatibility
 Inefficient data format due to PostgreSQL’s row-format

12. The PG-Strom Project
Our Answer – PL/CUDA + Matrix-like Array
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics12
CREATE FUNCTION my_logic(matrix, matrix)
RETURNS vector
AS $$
$$ LANGUAGE ‘plcuda’;
User define CUDA code block
Storage
GPU Kernel
User defined
CUDA code block
Post SQL Process
 Tables JOIN
 Window
Function
 ORDER BY
 GROUP BY
 etc....
Load function’s
arguments
Write-back
result set
PL/CUDA
method for manual optimization
ArrayType header a1 a2 aN… b1 b2 bN… c1 c2 cN… d1 d2 dN…
𝑎1 ⋯ 𝑑1
⋮ ⋱ ⋮
𝑎 𝑁 ⋯ 𝑑 𝑁
Matrix of
4cols x Nrows
Matrix-like Array
2D Array without NULL, to represent a matrix

13. The PG-Strom Project
Example of PL/CUDA function definition
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics13
CREATE OR REPLACE FUNCTION
knn_gpu_similarity(int, int[], int[])
RETURNS float4[]
AS $$
#plcuda_begin
cl_int k = arg1.value;
MatrixType *Q = (MatrixType *) arg2.value;
MatrixType *D = (MatrixType *) arg3.value;
MatrixType *R = (MatrixType *) results;
:
nloops = (ARRAY_MATRIX_HEIGHT(Q) + (part_sz - k - 1)) / (part_sz - k);
for (loop=0; loop < nloops; loop++) {
/* 1. calculation of the similarity */
for (i = get_local_id(); i < part_sz * part_nums; i += get_local_size()) {
j = i % part_sz; /* index within partition */
/* index of database matrix (D) */
dindex = part_nums * get_global_index() + (i / part_sz);
/* index of query matrix (Q) */
qindex = loop * (part_sz - k) + (j - k);
values[i] = knn_similarity_compute(D, dindex, Q, qindex);
}
}
:
#plcuda_end
$$ LANGUAGE 'plcuda';
CUDA code block

14. The PG-Strom Project
How GPU Kernels are built
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics14
CREATE OR REPLACE FUNCTION
my_cuda_func(float[])
RETURNS int[]
$$
#plcuda_sanity_check func_sc
#plcuda_begin
#plcuda_end
#plcuda_working_bufsz func_wb
$$ LANGUAGE ‘plcuda’;
User define CUDA code block
bool func_sc(float[])
helper function for sanity check
bigint func_wb(float[])
helper function for buffer size estimation
GPU
Binary
GPU
Kernel
Working
Buffer
Input
arguments
Run-time
compiler
input/output
of SQL data
User define
CUDA code block
Common Library
Routines
source program
of GPU code
Load the
arguments

15. The PG-Strom Project
Why automatic generated code was not best for performance
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics15
 NULL checks for each variable references
 Overflow checks for each primitive operators
 Function call instead of primitive operators
STATIC_FUNCTION(pg_float4_t)
pgfn_float4mul(kern_context *kcxt, pg_float4_t arg1, pg_float4_t arg2)
{
pg_float4_t result;
result.isnull = arg1.isnull | arg2.isnull;
if (!result.isnull)
{
result.value = arg1.value * arg2.value;
CHECKFLOATVAL(&kcxt->e, result,
isinf(arg1.value) || isinf(arg2.value),
arg1.value == 0.0 || arg2.value == 0.0);
}
return result;
}
select x*y from c_test;

16. The PG-Strom Project
Disadvantage because of data format (1/2)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics16
▌Row-oriented data
× includes unreferenced values
× many steps for data references
〇 common data format with PostgreSQL
▌Column-oriented data
〇 load only referenced variables
〇 data reference by 1 step
× needs data format exchange
GPU
core
GPU
core
GPU
core
a c d feb
a c d feb
a d feb
a c d feb
GPU
core
eb
GPU
core
GPU
core
GPU
core
GPU
core
b
b
b
b
b
GPU
core
GPU
core
e
e
e
e
e
Usual SQL load cannot justify the cost for data transformation,
Advanced algorithm processing is improved by columnar format.

17. The PG-Strom Project
Disadvantage because of data format (2/2)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics17
▌Case of random memory access
 Increase of memory transaction, but less usage rate of the data-bus
▌Case of coalesced memory access
 Least number of memory transaction, and maximum usage rate of the data-bus
32bit
Memory transaction width: 256bit
32bit 32bit32bit 32bit 32bit
32bit 32bit 32bit 32bit 32bit 32bit 32bit 32bit
Memory transaction width: 256bit
32bit x 8 = 256bit is valid data
in 256bit width memory transaction
(Bus usage ratio: 100.0%)
Only 32bit x 1 = 32bit is valid data
in 256bit width memory transaction
(Bus usage ratio: 12.5%)
GPU cores
GPU cores

18. The PG-Strom Project
2D-Array as Matrix
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics18
▌datatype[] array_matrix(variadic datatype[])
 An aggregate function to construct a 2D-array from the input stream.
 datatype is either of int2, int4, int8, float4 or float8.
 The 2D-array does not contain NULL.
▌SETOF record matrix_unnest(datatype[])
 Deform a 2D-array into stream of multiple records
▌Downside
 Unable to handle variable-length data type
 1GB limit of varlena data type in PostgreSQL
ArrayType header a1 a2 aN… b1 b2 bN… c1 c2 cN… d1 d2 dN…
𝑎1 ⋯ 𝑑1
⋮ ⋱ ⋮
𝑎 𝑁 ⋯ 𝑑 𝑁
Matrix of
4cols x Nrows
Matrix-like Array
2D Array without NULL, to represent a matrix

19. The PG-Strom Project
Example to call PL/CUDA function
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics19
SELECT row_number() OVER (),
float4_as_int4(R.key_id) key_id,
R.score
FROM matrix_unnest(
(SELECT my_plcuda_function(A.matrix,
B.matrix)
FROM (SELECT cbind(array_matrix(id),
array_matrix(x, y, z)) matrix
FROM normal_table
WHERE tag LIKE ‘%abc%’) A,
(SELECT matrix
FROM matrix_table) B
)
) AS R(key_id real, score real)
ORDER BY score DESC
LIMIT 1000;
Invocation of PL/CUDA
function with two
Matrix arguments
Construct, or load
pre-built array-matrix
Deform array-matrix
to generic records
Post-process by SQL
(JOIN, window-function)

20. The PG-Strom ProjectPGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics20
Case Study
similarity search on drug discovery

21. The PG-Strom Project
Background – relationship of disease and chemical compounds
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics21
target disease
relevant protein
chemical compounds
(= candidate of drugs)
Discovery of chemical compounds which are “active” to the target protein
inactive active
active
but toxicity
academic papers

22. The PG-Strom Project
k-NN Similarity Search on Chemical Compounds (1/2)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics22
Database chemical compounds set
(D; 10M records scale)
Query chemical
compounds set
(Q; ~1000 records scale)
Search by
Similarity
Target Protein
“similar compounds” will
have higher probability of active
Picks up active
chemical compounds
to the target protein
from academic papers

23. The PG-Strom Project
k-NN Similarity Search on Chemical Compounds (2/2)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics23
Similarity
is
definition of distance
ID NAME Fingerprint (1024bit)
1 CHEMBL153534 000000000001000000100000000000000100000000000001000000...
2 CHEMBL405398 000000000000000100100000000000000000000000000000100000...
3 CHEMBL503634 000001000000000000000000001000000100000000000000000000...
: : :
Data structure of the chemical compounds
Similarity by Jaccard index:
𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝐴, 𝐵 = 𝐴 𝐹𝑃 ∩ 𝐵 𝐹𝑃 𝐴 𝐹𝑃 ∪ 𝐵 𝐹𝑃

24. The PG-Strom Project
Scale of the computing
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics24
Database chemical compounds set
(D; 10M records scale)
Q: Query chemical
compounds set
average of
the top-3
𝑑𝑖 of D-compounds
Distance of Q-set
and 𝑑𝑖 compounds
𝑑𝑗 of D-compounds
average of
the top-3
Distance of Q-set
and 𝑑𝑗 compounds
Order of calculation:
𝑂 𝑄 × 𝐷 + 𝑂 𝐷 × 𝑄𝑙𝑜𝑔𝑄
(make distance) (sorting+average)

25. The PG-Strom Project
Implementation of PL/CUDA function (1/3)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics25
Step-1
Split all the logical combination of Q and D
into multiple partitions, then assign them
on SMM; execution unit of GPU.
Step-2
Each GPU core calculates a similarity score
between a Q-compound and a D-compound,
then store the score on “shared memory”;
which is fast and close to SMM.

26. The PG-Strom Project
Implementation of PL/CUDA function (2/3)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics26
Step-3
Bitonic-sorting by the similarity score, then
reorder the Q-compounds Step-5
Make an average by the
top-k values, then store it
on the result buffer.
Step-4
Repeat from Step-2, if # of Q-compounds is larger
than shared memory size. Top-k items are kept.

27. The PG-Strom Project
Implementation of PL/CUDA function (3/3)
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics27
CREATE OR REPLACE FUNCTION
knn_gpu_similarity(int, -- k-value
int[], -- ID+bitmap of Q
int[]) -- ID+bitmap of D
RETURNS float4[] -- result: ID+similarity
AS $$
#plcuda_decl
:
#plcuda_begin
#plcuda_kernel_blocksz ¥
knn_gpu_similarity_main_block_size
#plcuda_num_threads ¥
knn_gpu_similarity_main_num_threads
#plcuda_shmem_blocksz 8192
cl_int k = arg1.value;
MatrixType *Q = (MatrixType *) arg2.value;
MatrixType *D = (MatrixType *) arg3.value;
MatrixType *R = (MatrixType *) results;
:
for (loop=0; loop < nloops; loop++)
{
/* 1. calculation of the similarity */
for (i = get_local_id();
i < part_sz * part_nums;
i += get_local_size()) {
j = i % part_sz; /* index within partition */
dindex = part_nums * get_global_index()
+ (i / part_sz);
qindex = loop * (part_sz - k) + (j - k);
if (dindex < ARRAY_MATRIX_HEIGHT(D) &&
qindex < ARRAY_MATRIX_HEIGHT(Q)) {
values[i] = knn_similarity_compute(D, dindex,
Q, qindex);
}
}
__syncthreads();
/* 2. sorting by the similarity for each partition */
knn_similarity_sorting(values, part_sz, part_nums);
__syncthreads();
:
}
#plcuda_end
#plcuda_sanity_check knn_gpu_similarity_sanity_check
#plcuda_working_bufsz 0
#plcuda_results_bufsz knn_gpu_similarity_results_bufsz
$$ LANGUAGE 'plcuda';
real[] -- ID+Similarity of D-compounds (2xN)
knn_gpu_similarity(int k, -- k-value
int[] Q, -- ID+Fingerprint of Q-compounds (33xM)
int[] D); -- ID+Fingerprint of D-compounds (33xN)

28. The PG-Strom Project
Invocation of PL/CUDA function
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics28
PREPARE knn_sim_rand_10m_gpu_v2(int) -- arg1:@k-value
AS
SELECT row_number() OVER (),
fp.name,
similarity
FROM (SELECT float4_as_int4(key_id) key_id, similarity
FROM matrix_unnest(
(SELECT rbind( knn_gpu_similarity($1,Q.matrix,
D.matrix))
FROM (SELECT cbind(array_matrix(id),
array_matrix(bitmap)) matrix
FROM finger_print_query) Q,
(SELECT matrix
FROM finger_print_10m_matrix) D
)
) AS sim(key_id real, similarity real)
ORDER BY similarity DESC) sim,
finger_print_10m fp
WHERE fp.id = sim.key_id
LIMIT 1000;
Post-process by SQL; like lookup of compounds
name by compounds-id (tables JOIN), making
rank of similarity by window function.
Execution of PL/CUDA function
with Q-/D-matrix as argument
Transform the records read from tables
to Array-Matrix type.
(Pre-build is also possible)
Transform the Array-Matrix (3xN),
return value of PL/CUDA function,
into usual record data x Nrows.

29. The PG-Strom Project
Performance results
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics29
 For comparison to CPU cases, we implemented an equivalent SQL function by C.
 # of D-compounds is 10M records, # of Q-compounds is 10, 50, 100, 500 and 1000.
 Up to 10B combination search; almost equivalent size for real drug discovery research.
 HW) CPU: Xeon E5-2670v3, GPU: GTX980 / GTX1080, RAM:384GB
 SW) CentOS7, CUDA8.0, PostgreSQL v9.5 + PG-Strom v1.0
30.25
145.29
295.95
1503.31
3034.94
12.97 13.46 13.90 18.16 24.6513.00 13.23 13.59 16.01 19.13
0
500
1000
1500
2000
2500
3000
3500
10 50 100 500 1000
QueryResponseTime[sec]
Number of Query Compounds [Q]
Similarity search of chemical compounds by k-NN method (k=3, D=10M)
CPU(E5-2670v3) GTX980 GTX1080
x150 times
faster!

30. The PG-Strom ProjectPGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics30
Another Usage
k-means clustering in database system

31. The PG-Strom Project
Clustering Analysis
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics31

32. The PG-Strom Project
k-means clustering algorithm
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics32
1. Assign cluster randomly 2. Make centroid for each
cluster
3. Chose the nearest
cluster from the centroid

33. The PG-Strom Project
k-means clustering algorithm
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics33
1. Assign cluster randomly 5. Make centroid for each
cluster again
6. All the cluster get fully
converged
4. Repeat until convergence

34. The PG-Strom Project
k-means clustering on PL/CUDA
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics34
CREATE OR REPLACE FUNCTION
gpu_kmeans(real[], -- ID + Data Matrix
int, -- k-value (number of clusters)
int = 10, -- max number of iteration
int = 1) -- seed of initial randomness
RETURNS int[]
AS $$
#plcuda_decl
:
KERNEL_FUNCTION_MAXTHREADS(void)
update_centroid(MatrixType *D,
MatrixType *R,
MatrixType *C)
{
:
/* accumulate the local centroid */
for (did = get_global_id();
did < nitems;
did += get_global_size())
{
/* pick up the target cluster */
cid = r_values[nitems + did];
atomicAdd(&l_cent[cid], 1.0);
for (index=1; index < width; index++)
atomicAdd(&l_cent[index * k_value + cid],
d_values[index * nitems + did]);
}
__syncthreads();
/* write back to the global C-matrix */
for (index = get_local_id();
index < width * k_value;
index += get_local_size())
atomicAdd(&c_values[index], l_cent[index]);
}
:
#plcuda_begin
:
status = pgstromLaunchDynamicKernel4((void *)
setup_initial_cluster,
(kern_arg_t)(D),
(kern_arg_t)(R),
(kern_arg_t)(C),
(kern_arg_t)(r_seed),
nitems, 0, 0);
if (status != cudaSuccess)
PLCUDA_RUNTIME_ERROR_RETURN(status);
for (loop=0; loop < nloops; loop++)
{
:
status = pgstromLaunchDynamicKernelMaxThreads3(
(void *)kmeans_update_cluster,
(kern_arg_t)(D),
(kern_arg_t)(R),
(kern_arg_t)(C),
(kern_arg_t)k_value,
nitems, 0,
sizeof(cl_int) + sizeof(cl_float));
if (status != cudaSuccess)
PLCUDA_RUNTIME_ERROR_RETURN(status);
:
}
#plcuda_sanity_check gpu_kmeans_sanity_check
#plcuda_working_bufsz gpu_kmeans_working_bufsz
#plcuda_results_bufsz gpu_kmeans_results_bufsz
#plcuda_end
$$ LANGUAGE 'plcuda';

35. The PG-Strom Project
Test data for k-means clustering
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics35
▌Dataset overview
 A collection of datasets of vehicle traffic,
observed between two points for a set duration
of time over a period of 6 months
 449 observation points, at Aarhus, Denmark.
 13.5M records from Feb to June of 2014
▌Data contains
 average speed
 average measured time
 number of vehicles
 latitude/longitude of the observation points
 etc...
▌What we did
 categorize the zone of road into 5-classes
according to the characteristics of vehicle’s
running style.

36. The PG-Strom Project
Invocation of GPU k-means function
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics36
SELECT report_id, k, c
FROM (SELECT report_id, k, c,
row_number() OVER (PARTITION BY report_id
ORDER BY c DESC) rank
FROM (SELECT report_id, k, count(*) c
FROM matrix_unnest(
(SELECT gpu_kmeans ( array_matrix(
int4_as_float4(report_id),
avg_measured_time,
avg_speed,
vehicle_count),
5)
FROM tr_rawdata)
) R(report_id int, k int)
GROUP BY report_id, k
) __summary_1
) __summary_2
WHERE rank = 1;
Make a matrix from the raw-data
Run k-means clustering logic
Pick-up most frequent cluster

37. The PG-Strom Project
GPU k-means (1/3) – clustering by all the data
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics37
$ wget -O map.png "`psql traffic -At -f ~/traffic.sql`"
bypass highway?
Road towards
downtown?
Beltway?

38. The PG-Strom Project
GPU k-means (2/3) – Daytime and Nighttime
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics38
daytime (8-17) nighttime (18-7)

39. The PG-Strom Project
GPU k-means (3/3) – Weekdays and Weekend
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics39
weekdays weekend

40. The PG-Strom Project
Invocation of GPU k-means function
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics40
SELECT report_id, k, c
FROM (SELECT report_id, k, c,
row_number() OVER (PARTITION BY report_id
ORDER BY c DESC) rank
FROM (SELECT report_id, k, count(*) c
FROM matrix_unnest(
(SELECT gpu_kmeans ( array_matrix(
int4_as_float4(report_id),
avg_measured_time,
avg_speed,
vehicle_count),
5)
FROM tr_rawdata
WHERE extract('hour' from timestamp)
between 7 and 17
)
) R(report_id int, k int)
GROUP BY report_id, k
) __summary_1
) __summary_2
WHERE rank = 1;
Just add a line to select
different input data set.
Flexibility of SQL!

41. The PG-Strom ProjectPGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics41
Summary

42. The PG-Strom Project
Summary
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics42
▌What is PL/CUDA
 Original concept of PG-Strom is automatic optimization.
 PL/CUDA pulls out full capability of GPU instead of manual optimization.
Likely, nobody has written advanced algorithm in SQL :-)
▌Advantages
 TFLOPS grade computing engine for analytics in-database
 No need to export entire dataset for analytics by external applications
 Allows to utilize SQL flexibility for pre-/post-processing of the core
analytics algorithms
▌Future Challenges
 Data size larger than 1GB, because of varlena restriction in PostgreSQL
 Asynchronous execution. CPU parallel
 Time to construct matrix. If likely static, we can construct preliminary.

43. The PG-Strom Project
Resources
PGconf.SV 2016 - PL/CUDA / Fusion of HPC Grade Power with In-Database Analytics43
▌Repository
https://github.com/pg-strom/devel
▌Today’s Slides
http://www.slideshare.net/kaigai/pgconfsv2016-plcuda
▌Contact
 kaigai@ak.jp.nec.com
 Tw: @kkaigai

44. Question?