1. Efficient Representation of Data Structures on Associative Processors Jalpesh K. Chitalia (Advisor Dr. Robert A. Walker) Computer Science Department Kent State University

2. Presentation Outline  ASC Processor Architecture  Associative Features  Structure Codes  Represent Data Structures  Structure Code Operations  Implementation of Structure Codes  Summary and Future Work

3. The ASC Processor  A scalable design implemented on a million gate Altera FPGA  SIMD-like architecture  Currently, 36 8-bit Processing Elements (PE) available  8-bit Instruction Stream (IS) control unit with 8-bit Instruction and Data addresses, 32-bit instructions

4. The ASC Architecture

5.  Each PE listens to the IS through the broadcast and reduction network  PEs can communicate amongst themselves using the PE Network  PE may either execute or ignore the microcode instruction broadcast by IS under the control of the Mask Stack

6. The ASC Features  Associative Search Each PE can search its local memory for a key under the control of IS  Responder Resolution A special circuit signals if ‘at least one’ record was found  Masked Operation Local Mask Stacks can turn on or off the execution of instruction from IS

7. The ASC Example Select * from Students where Grade > 90

8. The ASC Features  Constant Time Associative Operations Associative Search Finding minimum or maximum in a field  Ideal for Database processing Data is organized in tabular format Each tuple in a table can be processed by one PE  PE Network Many parallel algorithms require all PEs to move contents in a regular pattern E.g.: Image Convolution, Matrix Multiplication

9. Non Tabular Data Structures  Many applications use linked list based data structures For example, plain HTML parsing can be done using tree-structure Similarly, XML or Object-relational databases can be represented using trees only Game programming uses tree-based algorithms, and demand much of processing power Complier construction uses directed acyclic graphs and tree structures

10. Data Structure Codes  A unique coding scheme Allows representation of any data structure into a tabular format Tabular format allows division of data amongst the PEs Also known as “structure code”  Different coding schemes for different data structures may be required  Uses Associative Search feature of the ASC Processor

11. Simple List-based Structures  The left figure shows a possible representation of 1D and 2D arrays using structure codes  The right figure represents stack and queue, depending on the use of appropriate functions

12. Complex List-based Structures Each digit-position indicates the level of a tree Each value in that position indicates the position of that child from the left Discussions henceforth are confined to trees Graphs are read in a slightly different manner

13. Structure Code Operations  Two sets of constant-time operations: scalar and parallel  Scalar Instructions are simple mathematic operations Search parent, child or root Finds value of structure code for next or previous nodes  Parallel Instructions use complex associative operations Finds code for next, previous or both siblings ‘Locates’ the required node for further processing

14. Scalar Operations fstcd: leftmost child nxtcd: right sibling prvcd: left sibling trncd: truncate (parent) node trnacd: truncate all (root) node  Can be used to allocate a new node Limited use in searching records

15. Parallel Operations  Index instructions: Flags a node of the result to ease further processing nxtdex (next or right), prvdex (previous or left) and sibdex (siblings or both left and right)  Value instructions: Returns the exact structure code of the result nxtval (next or right) and prvval (previous or left)  Can be used to ‘locate’ nodes in any tree  Uses parallel and associative hardware resources

16. Applications of DSC Ops Scalar  Associative searching: the parent or the root in a given data structure space  Allocating elements in a data structure space Value  Finding the value of resultant structure code Index  Most useful in associative search (eg, tree traversal)

17. Implementation Requirements  Finding the left or right neighbor – scalar and parallel operations  Implementation involves Input and Output assembly code Input: from scalar memory to IS, and to the PEs if necessary Output: from PEs or IS to scalar memory  I/O was non-functional in previous versions

18. Implementation Requirements  Hardware functionality Debugging and developing I/O functionality Debugging few other instructions  Structure Code Operations Scalar Operations: nxtcd, prvcd Parallel Operations: nxtdex, nxtval, prvdex, prvval, sibdex  Parallel Operations Most complex set amongst ASC operations

19. Implementation - Scalar Ops  Scalar Operations Involves mathematical manipulations to input structure code Input may not be a part of any data structure  Scalar I/O Input is read directly from the scalar memory Output is written directly to the destination address in scalar memory

20. Implementation – Value Ops  Processing of Parallel Value Ops Associative search, associative min/max  Input Cycle All the elements of data structure are distributed amongst the PEs sequentially Reference node is broadcast to them  Output Cycle Multi-byte structure code is stored in destination address (scalar memory)

21. Implementation – Index Ops  Processing of Parallel Index Ops The resultant element(s) is (are) ‘marked’ for further processing  Note: sibdex may select two results  Input and Output Input: same as in value operations Output: Bit flags for all the input nodes are stored sequentially in destination address (scalar memory)

22. Summary  SIMD-based computers are more suited for database processing  ASC processor with its associative operations makes them more efficient  Structure codes translate non-tabular data structures into a tabular format Tree and Graphs can be represented and evenly divided like records in a table  Object-relational databases, HTML processing Stacks, Queues, multi-dimensional arrays can be efficiently processed  Structure codes not required for even representation

23. Future Work  Efforts are in place to clean the architecture To allow multiplier/divider on each PE To accommodate RISC instruction set  Unpacked bytes are used to support variable-length structure code Can be avoided with an efficient divider unit  Input/Output Special structure code operations are defined but not implemented in this work Parallel input/output is also required  Developing applications that use structure codes

24. Acknowledgements  Professor Walker  Professor Potter  Committee members for their time  Kevin Schaffer, Hong Wang, Meiduo Wu, Lei Xie, Ping Xu

25. Questions? Thank You!