1. Pipelined and Parallel Computing Partition for 1 Hongtao Du AICIP Research Dec 1, 2005 Part 2

2. 2 Partition Scheme

3. 3 Driving Force Data-driven –How to divide data sets into different sizes for multiple computing resources –How to coordinate data flows along different directions such that brings appropriate data to the suitable resources at the right time. Function-driven –How to perform different functions of one task on different computing resources at the same time.

4. 4 Data - Flynn's Taxonomy Single Instruction Flow Single Data Stream (SISD) Multiple Instruction Flow Single Data Stream (MISD) Single Instruction Flow Multiple Data Stream (SIMD) –MPI, PVM Multiple Instruction Flow Multiple Data Stream (MIMD) –Shard memory –Distributed memory

5. 5 Data Partitioning Schemes Block ScatterContiguous point Contiguous row

6. 6 Communication Patterns and Costs Communication expense is the first concern in data-driven partition. Successor/Predecessor (S-P) pattern North/South/East/West (NSEW) pattern is the message preparation latency, is the transmission speed (Byte/s), is the number of processors, is the number of data, is the length of each data item to be transmitted.

7. 7 Understanding Data-driven The arrivals of data initiate and synchronize operations in the systems. The whole system in execution is modeled as a network linked by data streams. Granularity of the algorithm: the size of data block that transmitted between processors. The flows of data blocks form data streams. Granularity selection: trade-off between computation and communication –Large: reducing the degree of parallelism; increasing computation time; little overlapping between processors. –Small: increasing the degree of overlapping; increasing communication and overhead time

8. 8 Data Dependency Decreasing even dismissing the speedup Caused by edge pixels on different blocks BlockReverse diagonal

9. 9 Function Partitioning procedure –Evaluating the complexity of individual process in function and the communication between processes –Clustering processes according to objectives –Partitioning optimization

10. 10 Space-time-domain Expansion Definition: sacrificing the processing time to meet the performance requirements. Time complexity:

11. 11 One Dimension Partitioning Keeping the processing size to one column at a time. Repeatedly feeding in data until the process finishes. Increases the time complexity by n (the number of column)

12. 12 Two Dimension Partitioning Fixing the processing size to a two-dimensional subset of the original processing. Increasing the time complexity by

13. 13 Resource Constraints Multi-processor –Software implementation –Homogenous system –Heterogeneous system Hardware/software (HW/SW) co-processing –Software and hardware components are co-designed –Process scheduling VLSI –Hardware implementation –Communication time is ignorable

14. 14 Multi-processor Heterogeneous system –Contains computers in different types of parallelism. –Overheads in communicating add extra delays. –Communication tasks such as allocating buffers and setting up DMA channels have to be performed by the CPU and cannot be overlapped with the computation. Host/Master - a powerful processor Bottleneck processor - the processor taking the longest amount of time to perform the assigned task.

15. 15 HW/SW Co-processing System structure –SW - a single general purpose processor, Pentium or PowerPC –HW- a single hardware coprocessor, FPGA or ASIC –A block of shared memory Design view –Hardware components: RTL components (adders, multipliers, ALUs, registers) –Software component: general-purpose processor –Communication: between the software component and the local memory 90-10 Partitioning –Most frequent loops generally correspond to 90 percent of execution time but only consisting of simple designs

16. 16 VLSI Constraints –Execution time (DSP ASIC) –Power consumption –Design area –Throughput Examples –Globally asynchronous locally synchronous on-chip bus (Time) –4-way pipelined memory partitioning (Throughput)

17. 17 Question …… Thank you!