2. 2.1

4. OpenACC

5. example

6. :

7. Pi

8.
2.2

9. CUDA

10. Example

11. :

12. MatrixMul

16. step1

17. :

18. CPU

19. code

23. step2

24. :

25. CPU

26. launcher

30. step3

31. :

32. GPU

33. launcher

37. step4

38. :

39. Memory

40. Map

44. step5

45. :

46. GPU

47. kernel

51. step6

52. :

53. Optimization

55. 1.

56. CUDA

57. Architecture

59. MultiCore

60. Era

61.
singleCore

62. multiCore

63.
singleCore

65. Era:

67.
매년

68. CPU

69. 업그레이드만으로

70. S/W

71. 성능

72. 향상.

74.
multiCore

75. Era

76. :

78.
이제

79. CPU를

80. 바꿔도

82. 성능

83. 향상이

84. 전혀

85. 없음

87.
 일반

88. 앱

89. 개발자도

90. 성능

91. 향상을

92. 위해서는

93. 병렬

94. 프로그래밍

95. 고려

97. CPU(multiCore)

98. vs

99. GPU(maniCore)

100.
Intel

101. “Sandy

102. Bridge”

103. CPU

104. NVIDIA

105. “Kepler

106. GK104”

107. GPU

108.
GDDR5

109. 메모리

110. I/O

111.
Core

112. Core

113. Core

114. Core

115.
SMX

116.
GPC

117.
I/O

118.
System

120.
PCI-E

121. I/O

122.
내장

123.
I/O

124.
GPU

125.
GDDR5

126. 메모리

127. I/O

128.
NVENC

129.
Raster

130.
Sch

131.
L3

132. Cache

133.
DDR3

134. 메모리

135. I/O

136.
I/O

137.
GDDR5

138. 메모리

139. I/O

140.
Core

141.
SMX

142.
FP

143. Perf.

145. per

146. chip

147.
CPU

153. Sandy

154. Bridge

155. :

162. 32FP

163. =

171. 4

172. Core

173. (w/

174. 2

175. HT,

176. 4

177. AVE)

178.
GPU

193. GK104

194. :

196. 1536

197. FP

198. =

199. 1536

200. Core

223. GK110

224. :

226. 3072

227. FP

228. =

229. 3072

230. Core

232. CUDA

233. enable

234. GPUs

235.
Tegra

236. Geforce

237. Quadro

238. K5000

240. Tesla

241. K10/20

243.
(for

244. moble

245. :

246. not

247. yet,

248. so (for

249. gaming

250. PC)

251. (for

252. workstation)

253. (for

254. server)

255.
on)

256.
you

257. can

258. find

259. CUDA

260. enabled

261. GPUs

262. everywhere

263. !!!

265. GPGPU

266. Programming

267. Model

268. History

269.
1985

270. :

271. Amiga

272. -

273. (2D)

274. graphics

275. accelerator

276.
1980s

278. 1990

279. :

280. SGI,

281. IRIS

282. GL

283. for

284. Onyx

285. system

287. 1992

288. :

289. OpenGL:

291. OpenGL

292. ARB

293. founded

294.
1990s

295. 1996

296. :

297. 3dfx,

299. Voodoo

300. -

301. 3D

302. accelerator

303. ,

304. acquired

305. by

306. NVIDIA(2000)

307.
1996

308. :

309. Microsoft,

310. DirectX

311. accelerate

312. 3D

314. 1997

315. :

316. OpenGL,

317. OpenGL

318. 1.1

319.
2000s

320. 2002

321. :

322. NVIDIA,

323. first

324. GPUs

325. with

326. programmable

327. vertex,

328. fragment

329. shaders

331. 2002

332. :

333. Mark

334. Harris,

335. GPGPU

337. 2003

338. :

339. Lib

340. Sh

342. 2003

343. :

344. Ian

345. Buck,

346. BrookGPU

347. language

349. 2006

350. :

351. NVIDIA,

352. G80

353. -

354. Unified

355. shader

356. architecture:

357. fully

358. programmable

359. units

361. 2006

362. :

363. NVIDIA,

364. CUDA

366. 2006

367. :

368. PeakStream,

369. PeakStream

370. support

371. GPU,

372. acquired

373. by

374. Google(2007)

376. 2007

377. :

378. AMD,

379. Stream

380. SDK(Close-to-Metal,

381. Brook++)

383. 2008

384. :

385. RapidMind,

386. sh-like

387. language,

388. acquired

389. by

390. Intel(2009)

392. 2008

393. :

394. Khronos,

395. Apple,

396. OpenCL

399. 2009

400. :

401. Microsoft,

402. DirectCompute

403.
2010s

404. 2010

405. :

406. NVIDIA,

407. Parallel

408. NSight

409. Debugger

410. for

411. Visual

412. Studio

413. 1.0

415. 2010

416. :

417. PGI,

418. PGI

419. Accelerator

421. 2011

422. :

423. Microsoft,

424. C++

425. AMP

426.
2012

427. :

428. NVIDIA,

429. NSight

430. for

431. Eclipse

432. Edition

433.
2012

434. :

435. OpenACC,

436. PGI,

437. CAPS,

438. CRAY,

439. NVIDIA

440. support

441. OpenACC

443. CUDA

444. Parallel

445. Model

446.
OpenMP

447. Parallel

448.
CUDA

449. Parallel

450.
CPU

451.
CPU

452.
CPU

453. GPU

454. Core3000

455.
core02

456. core04

457. Core01

459. CUDA

460. Work

461. Model

462.
Step1.

463.
upload

464. input

465. data

466.
*ptr_cpu

467. *ptr_gpu

468.
Step2.

469.
FOR

470. LOOP

471. launch

472. GPU

473. Kernel

474.
Step3.

475.
download

476. result

477. data

479.
*ptr_cpu

480. *ptr_gpu

482. CUDA

483. Acceleration

484. Approach

485.
C/C++,

486. Fortran,

487. python

488.
5.

489. CUDA

491. development

492.
Matlab,

493. Mathematica,

494. Octave

495.
4.

496. CUDA

497. tools

498.
3.

499. OpenACC

500. Directives

501. PGI,

502. CAPS,

503. Cray

504. Compiler

505.
2.

506. CUDA

507. Libraries

508. cuFFT,

509. cuBLAS,

510. CULA

511.
Ansys

512. Mech,

513. Fluent,

516.
1.

517. CUDA

518. ISV

519. Apps

521. CUDA

522. ISV

523. Apps

524.
http://www.nvidia.com/object/gpu-applications.html

526.
ANSYS

527. Mech.

528. 14

530. Adobe

531. CS6

533. Library

534. :

535. cuFFT/cuBLAS

536.
• cuFFT

540. :

541. CUDA

542. version

543. FFT

544.
– 신호처리,

545. 영상처리

546. 분야

547. 활용

548.
– FFTW와

549. 함수구조

550. 동일

551.
• cuBLAS

553. :

554. CUDA

555. version

556. BLAS

557.
– 수치해석

558. 분야

559. 활용

560.
– MKL와

561. 함수구조

562. 동일

563.
• cuSpMV

564. :

565. CUDA

566. version

567. SpMV

568.
– 수치해석

569. 분야

570. 활용

572. Library

573. :

574. SAXPY

575. with

576. cuBLAS

577.
*cpu_ptr

578. *gpu_ptr

579.
cublasInit();

582.
cublasSetVector(N,

583. sizeof(x[0]),

584. x,

585. 1,

586. d_x,

587. 1);

589.
cublasSetVector(N,

590. sizeof(y[0]),

591. y,

592. 1,

593. d_y,

594. 1);

597.
cublasSaxpy

598. (N,

599. 2.0,

600. d_x,

601. 1,

602. d_y,

603. 1);

606.
cublasGetVector(N,

607. sizeof(y[0]),

608. d_y,

609. 1,

610. y,

611. 1);

615. OpenACC

616. :

617. website

618.
http://www.openacc.org

620.
PGI

621. Compiler

622. pgcc

623. ­–acc

624. saxpy.c

625.
CAPS

626. HMPP

627. Compiler

628. hmpp

629. gcc

630. saxpy.c

631.
CRAY

632. Compiler

634. OpenACC

635. :

636. SAXPY

637. example

638.
• openMP

639. level

640. GPU

641. Acceleration

642.
OpenMP

643. Parallel

645. with

646. 4

647. Core

648. CPU

649. OpenMP

650. Parallel

652. with

653. 3000

654. Core

655. GPU

656.
void

658. void

660.
saxpy(int

661. n,

662. float

663. a,

664. float

665. *x,

666. float

667. *y)

668. {

670. saxpy(int

671. n,

672. float

673. a,

674. float

675. *x,

676. float

677. *y)

678. {

682. #pragma

683. omp

684. parallel

687. #pragma

688. acc

689. kernels

693. {

696. {

703. for

704. (int

705. i

706. =

707. 0;

708. i

710. n;

711. ++i)

712. {

719. for

720. (int

721. i

722. =

723. 0;

724. i

726. n;

727. ++i)

728. {

737. y[i]

738. =

739. a*x[i]

740. +

741. y[i];

753. y[i]

754. =

755. a*x[i]

756. +

757. y[i];

765. }

772. }

775. }

779. }

781.
}

782. }

784. CUDA

785. :

786. SAXPY

787. example

788.
__global__

789. void

791.
saxpy(int

792. n,

793. float

794. a,

795. float

796. *x,

797. float

798. *y)

799. {

806. int

807. i

808. =

809. blockIdx.x*blockDim.x

810. +

811. threadIdx.x;

818. if

819. (i

821. n)

822. y[i]

823. =

824. a*x[i]

825. +

826. y[i];

828.
}

831.
cudaMemcpy(x,

832. d_x,

833. N,

834. cudaMemcpyHostToDevice);

836.
cudaMemcpy(y,

837. d_y,

838. N,

839. cudaMemcpyHostToDevice);

841. upload

843.
saxpy

844. 4096,256

845. (N,

846. 2.0,

847. x,

848. y);

850. CUDA

851. kernel

852. Launch

854.
cudaMemcpy(d_y,

855. y,

856. N,

857. cudaMemcpyDeviceToHost);

859. download

861. 2.

862. CUDA

863. Converting

864. Example

866. Example

867. :

868. PI

870.
#include

871. stdio.h

872.
#include

873. stdlib.h

874.
int

875. main

876. (int

877. argc,

878. char

879. *argv[])

880.
{

885. int

886. nthreads,

887. tid;

892. int

893. i,

894. INTER;

900. double

901. n_1,

902. x,

903. pi

904. =

905. 0.0;

910. INTER=100;

915. n_1

916. =

917. 1.0

918. /

919. (double)INTER

920. ;

932. for

933. (i

934. =

935. 0;

936. i

938. INTER;

939. i++)

948. {

961. x

962. =

963. n_1

964. *

965. ((double)i

966. -

967. 0.5);

980. pi

981. +=

982. 4.0

983. /

984. (1.0

985. +

986. x

987. *

988. x);

997. }

1002. pi

1003. *=

1004. n_1;