Efficient Representation of Data Structures on Associative Processors

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

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The ASC Architecture
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The ASC Architecture

• 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

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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

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The ASC Example

• Select from Students where Grade gt 90

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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

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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

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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

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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

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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

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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

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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

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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

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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)

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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

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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

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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

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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)

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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)

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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

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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

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Acknowledgements

• Professor Walker
• Professor Potter
• Committee members for their time
• Kevin Schaffer, Hong Wang, Meiduo Wu, Lei Xie,
  Ping Xu

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Questions?
Thank You!