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Optimizing Multi-Resource Data Partitioning and Function Distribution in Computing Environments

This research explores data partitioning schemes that effectively divide datasets into manageable sizes across multiple computing resources, enhancing efficiency and execution speed. It discusses both data-driven and function-driven approaches, examining communication patterns and dependency management within distributed systems. By analyzing Flynn's taxonomy and various partitioning methods, including time complexity and resource constraints, we highlight best practices for optimizing performance in heterogeneous environments. This study serves as a guide for leveraging data flows and task distribution to maximize resource utilization.

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Optimizing Multi-Resource Data Partitioning and Function Distribution in Computing Environments

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Presentation Transcript

  1. Hongtao Du Part 2 AICIP Research Dec 1, 2005

  2. Partition Scheme

  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. 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. Data Partitioning Schemes Block Scatter Contiguous point Contiguous row

  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. 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. Data Dependency • Decreasing even dismissing the speedup • Caused by edge pixels on different blocks Block Reverse diagonal

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

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

  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. 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. 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. 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. Question …… Thank you!

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