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Here is the video code list from GSpot. IYU1 1. 2 bit format used in mode 2 of the IEEE 1. Digital Camera 1. IEEE standard. V6. YUV 4: 2: 2 Vitec Multimedia. AJPG 2. 2fps JPEG- based codec for digital cameras - IYU2 2. IEEE 1. 39. 4 Digital Camera 1. Theora (free, reworked. Codec Rv30 Free Realtek EqualizerIEEE standard. V4. YUV 4: 2: 2 Vitec Multimedia. IV1 3ivx (MPEG4- based) 3ivx. IV2 3ivx (MPEG4- based) 3ivx. IVX 3ivx (MPEG4- based) 3ivx. VID 3ivx (MPEG4- based) 3ivx. AYUV 4: 4: 4 YUV format *No codec needed. Codec Rv30 Free Realtek AudioNV1. 2 8- bit Y plane followed by an interleaved U/V plane with 2x. DMB2 ? Trident Microsystems. VJPG A JPEG- based compression scheme for RGB bitmaps. Video Communication Systems. MHFY A. M. Paredes mhuffy. YUV (Loss. Less) - 1. A. M. Paredes predictor (Loss. Less) - IMJG Accom Sphere. OUS MJPEG with Alpha- channel - ATM4 Ahead Nero Digital MPEG- 4 Codec - NDIG Ahead Nero Digital MPEG- 4 Codec - TRLE Akula Alpha Pro Custom AVI (Loss. Less) - VTLP Alaris Video. Gram. Pi. X ? MDVD Alex Micro. DVD Video (hacked MS MPEG- 4) http: //www. ASLC Alpary. Soft Lossless Codec - AMR AMR Speech Codec 3. GPP. org. SAMR AMR Speech Codec 3. GPP. org. AP4. 1 Angel. Potion Definitive Angel. Potion. AP4. 2 Angel. Potion Definitive (hack MS MP4. GREY Apparently a duplicate of Y8. YUV2 Apple Component Video (YUV 4: 2: 2) - SMC Apple Graphics Codec Apple. PXLT Apple Pixlet (Wavelet) - PNG Apple PNG - QDGX Apple Quick. Draw GX - QDRW APPLE Quickdraw Palettized Video Apple. WRLE Apple Quick. Time BMP Codec - DVPN Apple Quick. Time DV (DV NTSC) - DVPP Apple Quick. Time DV (DV PAL) - 8. BPS Apple Quick. Time Planar RGB with Alpha- channel - TGA Apple TGA (with Alpha- channel) - TIFF Apple TIFF (with Alpha- channel) - AMPG Array Video. ONE MPEG Array Microsystems. AEMI Array Video. ONE MPEG1- I Capture Array Microsystems. IMC3 As IMC1, except that U and V are swapped - IMC4 As IMC2, except that U and V are swapped - NV2. As NV1. 2 with U and V reversed in the interleaved plane - IMC1 As YV1. U and V planes each have the same stride as the Y plane - ASV1 Asus Video Asus. ASV2 Asus Video (2) Asus. ASVX Asus Video 2. Asus. CYUY ATI Proprietary YUV compression ATI Technologies. VCR3 ATI VCR 3. 0 ATI Technologies. VCR4 ATI VCR 4. 0 ATI Technologies. VCR5 ATI VCR 5. 0 ATI Technologies. VCR6 ATI VCR 6. 0 ATI Technologies. VCR7 ATI VCR 7. 0 ATI Technologies. VCR8 ATI VCR 8. 0 ATI Technologies. VCR9 ATI VCR 9. 0 ATI Technologies. VCR1 ATI Video Codec 1 ATI Technologies. VCR2 ATI Video Codec 2 ATI Technologies. VYU9 ATI YUV ATI Technologies. VYUY ATI YUV ATI Technologies. YU1. 2 ATI YV1. 2 4: 2: 0 Planar - AURA Aura. Vision Aura 1: YUV 4. Aura. Vision Corporation. AUR2 Aura. Vision Aura 2: YUV 4. Aura. Vision Corporation. AASC Autodesk Animator codec Autodesk. AFLC Autodesk Animator codec Autodesk. AFLI Autodesk Animator codec Autodesk. AAS4 Autodesk Animator codec (RLE) - FLIC Autodesk FLI/FLC Animation http: //www. AUVX AUVX video codec. USH Gmb. HAVUP Avid 1. Packed (Quick Time) - AV1. X Avid 1: 1x (Quick Time) - AVR Avid ABVB/Nu. Vista MJPEG with Alpha- channel - AVDN Avid DNx. HD (Quick Time) - AVDV Avid DV - AVD1 Avid DV (Quick Time) - AVMP Avid IMX (Quick Time) - ADVJ Avid M- JPEG Avid Technology Also known as AVRn - AVUI Avid Meridien Uncompressed with Alpha- channel - AVID Avid Motion JPEG - MWV1 Aware Motion Wavelets Aware Inc. XVIX Based on Xvi. D MPEG- 4 codec - BHIV Be. Here i. Video - BW0. Berg. Wave (Wavelet) - . Data Translation, Inc. BW1. 0 Broadway MPEG Capture/Compression Data Translation. CGDI Camcorder Video Microsoft. CDVC Canopus DV Codec Canopus. CUVC Canopus HQ - CLLC Canopus Loss. Less - CDV5 Canopus SD5. DVHD - CDVH Canopus SD5. DVHD - ICLB Cell. B Videoconferencing Codec In. Soft. AHDV Cine. Form 1. Visually Perfect HD (Wavelet) - CFHD Cine. Form 1. 0- bit Visually Perfect HD (Wavelet) - CVID Cinepak by Supermac Supermac. SEG4 Cinepak for Sega Sega. SEGA Cinepak for Sega Sega. CLJR Cirrus Logic YUV 4: 1: 1 Cirrus. CTRX Citrix Scalable Video Codec - UCOD Clear. Video e. Majix. com. CMYK Common Data Format in Printing Colorgraph (UK)BTCV Composite Video Codec Conexant. BTVC Conexant Composite Video Conexant. Y4. 22 Copy of UYVY used in Pyro Web. Cam firewire camera ADS Technologies. PNG1 Corecodec. org Core. PNG Codec - CYUV Creative Labs YUV Creative Labs, Inc. LBYR Creative Web. Cam codec - SJPG CUsee. Me Networks Codec - FMJP D- Vision fieldbased ISO MJPEG - DVMA Darim Vision DVMPEG (dummy for MPEG compressor) http: //www. FRWT Darim Vision Forward Motion JPEG http: //www. FRWU Darim Vision Forward Uncompressed http: //www. DCL1 Data Connection Conferencing Codec - DFSC Debug. Mode Frame. Server VFW Codec - DIB Device Independent Bitmap * No codec needed. DAVC Dicas MPEGable H. MPEG- 4 AVC base profile codec - DM4. V Dicas MPEGable MPEG- 4 - SDCC Digital Camera Codec Sun Communications. DXTN Direct. X Compressed Texture Microsoft. DXTC Direct. X Texture Compression Microsoft. DXT1 Direct. X Texture Compression Format 1 Microsoft DXT2 Direct. X Texture Compression Format 2 Microsoft DXT3 Direct. X Texture Compression Format 3 Microsoft DXT4 Direct. X Texture Compression Format 4 Microsoft DXT5 Direct. X Texture Compression Format 5 Microsoft U< Y Discreet UC YUV 4: 2: 2: 4 1. U< YA Discreet UC YUV 4: 2: 2: 4 1. Alpha- channel) - DIV4 Div. X 3 Fast- Motion Div. XDIV3 Div. X 3 Low- Motion Div. XDIVX Div. X 4 (Open. Div. X) Project Mayo. VIDM Div. X 5. 0 Pro Supported Codec - DIV5 Div. X 5. x/6. x divx. DX5. 0 Div. X 5. x/6. DIV6 Div. X ; -) (MS MPEG- 4 v. DXSB Div. X Subtitles Codec - DPSC DPS PAR Motion JPEG DPS/Leitch. CFCC DPS Perception Digital Processing Systems. DPS0 DPS Reality Motion JPEG DPS/Leitch. TM2. X Duck True. Motion 2. X Duck (now ON2) Corp. TM2. A Duck True. Motion Archiver 2. Duck (now ON2) Corporation. DVHD DV 1. 12. 5 lines at 3. Hz or 1. 25. 0 lines at 2. Hz IEC Standard. DSVD DV Codec ? DVSL DV compressed in SD (SDL) IEC Standard. PDVC DVC codec I- O Data Device, Inc. DVC DVC/DV Video IEC 6. SMPTE 3. 14. MDVCP DVC/DV Video IEC 6. SMPTE 3. 14. MDVSD DVC/DV Video IEC 6. SMPTE 3. 14. MDVE2 DVE- 2 Videoconferencing Codec In. Soft. DVX1 DVX1. 00. SP Video Decoder Lucent. DVX2 DVX2. 00. 0S Video Decoder Lucent. DVX3 DVX3. 00. 0S Video Decoder Lucent. DP0. 2 Dyna. Pel MPEG- 4 - YV1. Elecard YUV 4: 2: 2 Planar - DXGM Electronic Arts Game Video codec - EQK0 Elsa graphics card quick codec - ESCP Escape Eidos Technologies. M4. CC ESS MPEG4 Divio codec - ETV1 e. Treppid Video Codec e. Treppid Technologies. ETV2 e. Treppid Video Codec e. Treppid Technologies. ETVC e. Treppid Video Codec e. Treppid Technologies. EM2. V Etymonix MPEG- 2 I- frame http: //www. EMWC Ever. Ad Marquee WMA codec. Ever. Ad, Inc. XYZP Extended PAL format XYZ palette http: //www. YUVP Extended PAL format YUV palette http: //www. FVFW ff MPEG- 4 based on Xvi. D codec - FFDS ffds Lossless Video FFmpeg. FFV1 ffds Lossless Video FFmpeg. IVD FFmpeg Div. X ; -) (MS MPEG- 4 v. COL0 FFmpeg Div. X ; -) (MS MPEG- 4 v. COL1 FFmpeg Div. X ; -) (MS MPEG- 4 v. MPG3 FFmpeg Div. X ; -) (MS MPEG- 4 v. VIV1 FFmpeg H2. 63+ decoder ? MPG1 FFmpeg MPEG 1/2 ? MPG2 FFmpeg MPEG 1/2 ? BLZ0 FFmpeg MPEG- 4 ? DIV1 FFmpeg Open. Div. X ? FMP4 FFmpeg/ffdshow ISO MPEG- 4 FFmpeg/ffdshow (open source)SNOW FFSNOW (Michael's wavelet codec) ?(use ffmpeg)THEO FFVFW Supported Codec - FLJP Field Encoded Motion JPEG D- Vision. FLV1 Flash Sorenson Video Sorenson Media. FLV4 Flash VP6 On. Technologies. CLPL Format similar to YV1. Infinop Inc. IMAC Intel hardware motion compensation. Intel Corporation. AEIK Intel Indeo Video 3. Vector Quantization) - IFO9 Intel intermediate YUV9. Intel Corporation. H2. 69 Intel ITU H. POTS- based videoconferencing - H2. Intel ITU H. 2. 65 - H2. Intel ITU H. 2. 66 - H2. Intel ITU H. 2. 67 - H2. Intel ITU H. 2. 68 - IAN Intel RDX. Intel Corporation. RVX Intel RDX. Intel Corporation. IRAW Intel YUV Uncompressed Intel. ISME Intel's next- generation video codec. Intel Corporation. YV9. 2 Intel's Smart Video Recorder YVU9 - IJPG Intergraph JPEG Intergraph. IUYV Interlaced version of UYVY http: //www. IY4. 1 Interlaced version of Y4. P http: //www. leadtools. WZCD i. Scan. Ltd. Y4. 44 IYU2 (i. Rez Stealth Fire camera) - BLOX Jan Jezabek BLOX MPEG Codec - JPG JPEG compressed - KMVC Karl Morton's Video Codec (presumably) Team. Software. ABYR Kensington ? Kensington. KPCD Kodak Photo CD - LAGS Lagarith Loss. Less - ILVC Layered Video Intel. MSZH LCL (Lossless Codec Library) http: //www. Playtown- Denei/2. LRC. htm. ZLIB LCL (Lossless Codec Library) zlib compression http: //www. Playtown- Denei/2. LRC. htm. LJPG LEAD MJPEG Codec Lead Technologies. LSCR LEAD Screen Capture - LGRY Lead Technologies Grayscale Image - L2. Lead Technologies H. L2. 63 Lead Technologies H. LCMW Lead Technologies Motion CMW Codec - LEAD LEAD Video Codec Lead Technologies. LJ2. K LEADTools JPEG2. LCW2 LEADTools MCMW 9. Motion Wavelet) - LMP2 LEADTools MPEG2 - LIA1 Liafail. Liafail, Inc. LSVC Lightning Strike Video Codec ESPRE Solutions. LSVM Lightning Strike Video Codec ESPRE Solutions. LSVX Lightning Strike Video Codec ESPRE Solutions. LOCO LOCO Lossless Codec - ADV1 Loronix Wave. Codec (used in various CCTV products) - SANM Lucas. Arts Smush v. 2 Lucas. Arts. V2. 61 Lucent VX2. S Lucent. SCCD Luminositi Soft. Cam codec. Luminositi, Inc. LZO1 LZO compressed (lossless codec) - DVCS Main. Concept DV Codec - SWC1 Main. Concept Motion JPEG - AVI1 Main. Concept Motion JPEG Codec - AVI2 Main. Concept Motion JPEG Codec - MC2. Main. Concept Motion JPEG Codec - DC2. Main. Concept Pro. DV Codec - VBLE Marc. FD VBLE Lossless Codec - DV2. Matrox DVCPRO codec Matrox. DV5. 0 Matrox DVCPRO5. Matrox. MMIF Matrox MPEG- 2 elementary I- frame- only video stream. Matrox Electronic Systems, Ltd. MMES Matrox MPEG- 2 I- frame Matrox. MTX1 Matrox, possibly a texture format Matrox. MTX2 Matrox, possibly a texture format Matrox. MTX3 Matrox, possibly a texture format Matrox. MTX4 Matrox, possibly a texture format Matrox. MTX5 Matrox, possibly a texture format Matrox. MTX6 Matrox, possibly a texture format Matrox. Hardware Acceleration of EDA Algorithms- P1. Experiments 1. 63. Table 1. 0. 2 Speedup for circuit simulation. Professional Series computer and software with which to prepare their final presentation for judging in Spring 1991. Initial versions work with serial devices, but a version for people who already have parallel devices will follow. Easy overlay can be activated by. Full text of 'Official gazette of the United States Patent and Trademark Office: Omega. SIM (s) Au. SIM (s)Ckt name # Trans. CPU- alone GPU+CPU Speed. Up. Industrial. Au. SIM runs partly on GPU and partly on CPU against theoriginal Omega. SIM (running on the CPU alone). Columns 1 and 2 report the cir- cuit name and the number of transistors in the circuit, respectively. The number ofevaluations required for full circuit simulation is reported in column 3. Columns 4and 5 report the CPU- alone and GPU+GPU runtimes (in seconds), respectively. The speedups are reported in column 6. The circuits Industrial. These results show that an average speedup of 2. Also, note that with an increase in the number of transistorsin the circuit, the speedup obtained is higher. C O N!AUTEUR i NF ORNlATiQN S O URCE. FEATURES 9 2M ' v O L U M E 5. 7' MASS STORAG E Options for PC IIMac ', ~h LLL. 309 Iceni.Technology.Infix.Server.v2.01 Iceni.Technology.Argus.v6.1.09 Iceni.Technology.InfixPro.PDF.Editor.v4.17 IComS.XCAD.2008.Professional.v1.1 Idea Spectrum Realtime Landscaping Architect v2.06 IdeaBlade DevForce v3.6.4.1 Idecad 5.14. Then are followed sequentially with serial assessments of functional ovarian reserve. 1996 Welt et al., 1997; Wunder et al., 2008; Robertson et al., 2011; Overbeek et al., 2012), with peaks in the midfollicular phase and at ovulation. However, this is the. Acts as a limiting factor to net primary production (Lebauer and Treseder, 2008). Sampling bottles were cleaned. This is because the GPU mem- ory latencies can be better hidden when more device evaluations are issued inparallel. The NVIDIA 8. 80. GPU device supports IEEE 7. Serial Xcad 2008 Professional References SamplesHowever, the BSIM3 model code uses IEEE 7. We determined the error that was incurred in this process. We found that theaccuracy obtained by our GPU- based implementation of device model evaluation(using single precision . In particular, we computed the errorover 1. The rela- tive average error was 4. NVIDIA has announced the availability of GPUdevices which support double precision . Such devices willfurther improve the accuracy of our approach. Figures 1. 0. 1 and 1. Industrial. A large fraction (on average 7. SPICE runtimeis spent in evaluating transistor model equations. The chapter reports our effortsto accelerate transistor model evaluations using a GPU. We have integrated thisaccelerator with a commercial fast SPICE tool and have shown signi. The asymptotic speedup that can be obtained is about 4. Withthe recently announced quad GPU systems, this speedup could be enhanced further,especially for larger designs. References. 1. Capsim Hierarchical Spice Simulation. NVIDIA Tesla GPU Computing Processor. Omega. Sim Mixed- Signal Fast- SPICE Simulator. Virtuoso Ultra. Sim Full- chip Simulator. Agrawal, P., Goil, S., Liu, S., Trotter, J.: Parallel model evaluation for circuit simulation onthe PACE multiprocessor. In: Proceedings of the Seventh International Conference on VLSIDesign, pp. Agrawal, P., Goil, S., Liu, S., Trotter, J. A.: PACE: A multiprocessor system for VLSI circuitsimulation. Full text of 'Amiga World Magazine (January 1993)' See other formats. If you listened to a professional conversation between two realtors, what would you hear? When they awoke to launch the butterfly in the morning, it had died. ICP J tends to make recurrent references to the butterfly and reuniting with it in Shangri-La in his. In: Proceedings of SIAM Conference on Parallel Processing, pp. Amdahl, G.: Validity of the single processor approach to achieving large- scale computingcapabilities. Proceedings of AFIPS 3. Dartu, F., Pileggi, L. T.: TETA: transistor- level engine for timing analysis. In: DAC ’9. 8: Pro- ceedings of the 3. Annual Conference on Design Automation, pp. Gulati, K., Croix, J., Khatri, S. P., Shastry, R.: Fast circuit simulation on graphics processingunits. In: Proceedings, IEEE/ACM Asia and South Paci. Hachtel, G., Brayton, R., Gustavson, F.: The sparse tableau approach to network analysis anddesignation. Circuits Theory, IEEE Transactions on 1. Nagel, L.: SPICE: A computer program to simulate computer circuits. In: University of. California, Berkeley UCB/ERL Memo M5. Nagel, L., Rohrer, R.: Computer analysis of nonlinear circuits, excluding radiation. IEEEJournal of Solid States Circuits SC- 6, 1. Pillage, L. T., Rohrer, R. A., Visweswariah, C.: Electronic Circuit & System Simulation Meth- ods. Mc. Graw- Hill, New York (1. ISBN- 1. 3: 9. 78- 0. ISBN- 1. 0: 0. 07. Sadayappan, P., Visvanathan, V.: Circuit simulation on shared- memory multiprocessors. IEEETransactions on Computers 3. Part IVAutomated Generation of GPU Code. Outlineof. Part. IVIn Part I of this monograph candidate hardware platforms were discussed. In Part. II, we presented three approaches (custom IC based, FPGA based, and GPU- based)for accelerating Boolean satis. In Part. III, we presented the acceleration of several EDA applications with varied degreesof inherent parallelism in them. In Part IV of this monograph, we present an auto- mated approach to accelerate uniprocessor code using a GPU. The key idea hereis to partition the software application into kernels in an automated fashion, suchthat multiple instances of these kernels, when executed in parallel on the GPU, canmaximally bene. Thetask of implementing a software application on a GPU currently requires signi. In Chapter 1. 1, we explorean automated approach to partition a uniprocessor software application into kernels(which are executed in parallel on the GPU). The input to our algorithm is a unipro- cessor subroutine which is executed multiple times, on different data, and needs tobe accelerated on the GPU. Our approach aims at automatically partitioning thisroutine into GPU kernels. This graph is then par- titioned. Various partitions are explored, and each is assigned a cost which accountsfor GPU hardware and software constraints, as well as the number of instances ofthe subroutine that are issued in parallel. From the least cost partition, our approachautomatically generates the resulting GPU code. Experimental results demonstratethat our approach correctly and ef. We show that with our partitioning approach, we can speed up certain routinesby 1. Our entire technique (from reading a C subroutine to generating the partitioned GPUcode) is completely automated and has been veri. This chapter presents an automated approach to partition a software appli- cation into kernels (which are executed in parallel) that can be run on the GPU. The software application should satisfy the constraint that it is executed multipletimes on different data, and there exist no control dependencies between invoca- tions. The input to our algorithm is a C subroutine which needs to be acceleratedon the GPU. Our approach automatically partitions this routine into GPU kernels. This is done as follows. This graph is then partitioned using a K- waypartition, using several values of K. For every partition a cost is computed whichaccounts for GPU’s hardware and software constraints. The cost also accounts forthe number of instances of the subroutine that are issued in parallel. We then selectthe least cost partitioning solution and automatically generate the resulting GPUcode corresponding to this partitioning solution. Experimental results demonstratethat our approach correctly and ef. Wedemonstrate that with our partitioning approach, we can speed up certain routinesby 1. Our approach is completely automated and has been veri. The motivation for thiswork is described in Section 1. Section 1. 1. 3 details our approach for kernelgeneration for a GPU. In Section 1. 1. 4 we present results from experiments andsummarize in Section 1. Introduction. There are typically two broad approaches that have been employed to accelerate sci- enti. This redesigned code is now run on the GPU. Khatri, Hardware Acceleration of EDA Algorithms,DOI 1. Examplesof this approach include the GPU implementations of sorting . A good reference in this area is . We refer to this approach as the porting approach, since only aportion of the original CPU- based code is ported on the GPU without any rearchi- tecting of the code. This approach requires less coding effort than the rearchitectingapproach. The overall speedup obtained through this approach is, however, subjectto Amdahl’s law, which states that if a parallelizable subroutine which requires afractional runtime of P is sped up by a factor Q, then the . The porting approach is applicable to many problems in which a smallnumber of subroutines are run repeatedly on independent data values and take up alarge fraction of the total runtime. Therefore, an approach to automatically generate. GPU code for such problems would be very useful in practice. In this chapter, we focus on automatically generating GPU code for the portingclass of problems. Porting implementations require careful partitioning of the sub- routine into kernels which are run in parallel on the GPU. Several factors must beconsidered in order to come up with an optimal solution. In fact, if a given subroutine is run without consideringcertain key constraints, the subroutine may fail to execute on the GPU altogether. Our approach also schedules thepartitions in such a manner that correctness is retained. The fact that we operate ona restricted class of problems. In contrast the taskof general parallelizing compilers is signi. Examples include the Parafrase For- tran reconstructing compiler . Parafrase is an optimizing compiler preprocessor. Our approach is employed for subroutines that are executed multiple times, on independent data. Our Approach 1. 71that takes as input scienti. The automatic parallelization targeted in . The resultant code is optimized formultiple instruction multiple data (MIMD) and very long instruction word (VLIW)architectures. The Bulldog Fortran reassembling compiler . It is designed to detect parallelism that is notamenable to vectorization by exploiting parallelism within the basic block. The key contrasting features of our approach to existing parallelizing compilersare as follows. First, our target platform is a GPU. Thus the constraints we needto satisfy while partitioning code into kernels arise due to the hardware and archi- tectural constraints associated with the GPU platform. Also, the memory access patterns required for optimized exe- cution of code on a GPU are very speci. Our approach attempts to incorporate these requirementswhile generating GPU kernels automatically. Our Approach. Our kernel generation engine automatically partitions a given subroutine S into Kkernels in a manner that maximizes the speedup obtained by multiple invocations ofthese kernels on the GPU. Before our algorithm is invoked, the key decision to bemade is the determination of which subroutine(s) to parallelize. This is determinedby pro. We referto this fraction as P. Now each subroutine S . Given asubroutine S and a number N which represents the number of independent calls of Sthat are issued by the calling program (on different data), . Note that our kernel generation. Standard Industrial Classification (SIC) System Search. Thank You for Visiting Our Website. You are exiting the Department of Labor's Web server. The Department of Labor does not endorse, takes no responsibility for, and exercises no control over the linked organization or its views, or contents, nor does it vouch for the accuracy or accessibility of the information contained on the destination server. 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