HPPS 2008 - Panzica Motta Bonanno

ISA customizationISA customization

Andrea Tommaso BonannoAndrea Tommaso BonannoValerio Panzica La MannaValerio Panzica La Manna

Alfredo MottaAlfredo Motta

Prof: Donatella Sciuto

ISA customization Andrea Tommaso BonannoValerio Panzica La MannaAlfredo Motta

HPPS Project19-6-2008

2

Context

Customized processorA basic processor is defined and then extended with units specialized for each particular application (i.e. differently customized versions of the processor for each product)

Instruction set extensiongrouping dataflow independent operations in the application software as potential new complex instructionsThe resulting set of instructions is actually used for code generation and execution



3

Customizable processors design

Very common practice in system-on-chip design

Growing in the demand for application-specific embedded processors

Performance increasing in specific domains (decrease) costs of advanced RISC processors (decrease) complexity of entirely customized instruction sets

ISA customizationAndrea Tommaso BonannoValerio Panzica La MannaAlfredo Motta


4

Issues

GoalAutomatically finding the best performing ISEs for an application independently from the technology adopted

ProblemNo feasible exact solution if we want to generate more than one instruction but

Several algorithms exist to find the single best performing ISE in a graph (ILP, pruned subgraphs exploration)



5

Graph Exploration

Dependence graphRepresents the data flow of a basic block of the program assembly codeNodes: Instructions of the basic block whose used

registers are involved in dependences

Input and Output nodes

Edges: show dependences between instructions

RAW dependences



6

Graph Exploration

addik r1, r1, -48swi r15, r1, 0swi r19, r1, 40swi r22, r1, 44addk r19, r1, r0addik r3, r0, 1swi r3, r19, 28lwi r3, r19, 28addik r18, r0, 9cmp r18, r3, r18blti r18, 272



7

ISE Identification Alghorithms

Critical point of most algorithms

Problem space exponential in the size of the graph

New practical algorithm (Bonzini, Pozzi, USI)

Focuses on convex subgraphs and multiple vertex dominatorsComplexity polynomial in the size of the graph

Main approachesBased on the dominance relationEnumeration of valid subgraphs



8

Our Work

Simulated code generation by means of the synthesized MicroBlaze processor

Xilinx EDK

Parsing of the assembly code in such a way to find out all the dependence graphs and represent them in a structured way

C++ (Boost libraries)

Dot notation



9

Our Work

Implementation of the incremental version of the novel algorithm for the identification of the best performing ISE

PythonGraphviz



10

Tool 1: Goal

Building a graph of all the true data dependencies of a basic block.

PARSER

AssemblyGenerationC CODE

MicroBlaze Assembly



...

11

The code taken as example regards some functions :Quarter-Wave Sine TransformDiscrete Sine TransformDiscrete Cosine Transform

C CODE



12

AssemblyGenerator

Xilinx XPS

C CODE

Xilinx EDKSymulation of

MicroblazeProcessor

MicroBlaze Assembly



13

MicroBlaze Assembly

In order to find the data dependencies of the Assembly code, the MicroBlaze ISA is divided in three subclasses:

1. Write&Use instructions: ISTR_NAME RD, RA, RB(e.g addik, lwi)

2. OnlyUse instructions: ISTR_NAME RA, RB (offset)(e.g swi, put)

3. Control instructions: ISTR_NAME RA, RB // target(e.g bnei, blti)

Note that control instructions define the end of a basic block



14

2c: f8930028 swi r4, r19, 40 30: f8b3002c swi r5, r19, 44 34: 30600001 addik r3, r0, 1 38: f873001c swi r3, r19, 28 3c: e873001c lwi r3, r19, 28 40: 32400064 addik r18, r0, 100 44: 16439001 cmp r18, r3, r18 48: bc5202a4 blti r18, 676 //104

Assembly code example

Write&Use Instr

OnlyUse Instr

Control Instr



15

2c: f8930028 swi r4, r19, 40 30: f8b3002c swi r5, r19, 44 34: 30600001 addik r3, r0, 1 38: f873001c swi r3, r19, 28 3c: e873001c lwi r3, r19, 28 40: 32400064 addik r18, r0, 100 44: 16439001 cmp r18, r3, r18 48: bc5202a4 blti r18, 676 // 104

Assembly code example

Write&Use Instr

OnlyUse Instr

Control Instr



16

3 main steps:

1. Clean the input file from not necessary informations2. For each instruction i:

Determine the instruction subclass S(i)If S(i) is Write&Use find all the data dependences until the end of the basic block (so when S(i) == Control )

3. For each basic block b:Create a graph G(b) where the nodes represent the instructions in b and the arcs the dependencies.Convert G in a dot file

PARSER The Algorithm



17

The tool is completely realized in C++:Good performance (computed assembly code with more than 50 basic blocks in less than 260 ms)Possibility to exploit the Boost Graph LibrariesQuadratic complexity of the algorithm

Future Developments:Create a multi-architecture parser that supports different ISA



PARSER Implementation Infos

18

RESULT



19

Tool 2: Problem Statement

Input: Graph in DOT format representing the dependencies between instructionsAlgorithm Key Idea: Search for instructions that can be grouped together in order to create custom instructions → convex cuts Output: All the convex cut of the input graph

find_convex_cut.py



20

Definitions - Cut

Definition 1 (Cut): A cut S is a subgraph of a graph G.Definition 2 (Convex cut): A cut S is convex if there is no path from a vertex u S to another ∈vertex v S which ∈contains a vertex w ∈NOT(S)



21

Definitions - Input&Output

Out goal is to find customizable instructions. We must take care about the number of possible input and output.Definition 3 (Input Set): Given a set S, we call inputs of S the set I(S) of predecessor vertices of those edges which enter the cut S from the rest of the graph G.

Definition 4 (Output Set): Given a set S, we call outputs of S the set O(S) of vertices which are part of S, but have at least one successor v NOT(S)∈



22

User Parameters


Nin: Represents the maximum number of read port in the register file which a custom instruction can use → the algorithm searchs all convex cuts that have less input than Nin.

Nout: Represents the maximum number of write ports in the register file which a custom instruction can use → the algorithm searchs all convex cuts that have less outputs than Nout.

Forbidden nodes are related to architectural constraints. They represent instructions that must not be included in any convex cut. Example: if the custom functional unit cannot have any memory port, loads and stores must be included in the forbidden nodes set


23

Key Idea


Summary: Given a graph G, the posed problem is to find all convex cuts S G under the constraints ⊆that |I (S)| ≤ Nin ,|O (S)| ≤ Nout , and S ∩ F = .∅Complexity Problem: The problem is that common alghorithms are exponential in the size of the graph.

Solution: #Input=Nin and #Output=Nout of custom instructions are limited. Why not looking for a relation between them and convex cuts?


24

Theorem


Any convex cut is uniquely identified by its sets of input and output vertices, respectively I(S) and O(S). In other words, two convex cuts of the same graph are equal iff they have the same inputs and outputs.

-> It is possible to enumerate convex cuts by coupling every possible set of outputs with all the possible sets of inputs. Moreover if we put a contraint on the number of inputs and outputs, the number of valid convex cuts is clearly polynomial; more precisely, it is O n^(Nin +Nout) .


25

Compute the set


1- Every element of the output set is not postdominated by any element

2- The input set is a common multiple vertex dominator of the output set

So first we pick an output value that is admissible, then we pick an input value such that the input set is a common multiple vertex dominator of the output. Doing that recursively we construct the input and output sets.

The algorithm searches for input and output sets. Obviously not all the sets are admissible. Two properties are required.


26

Function Summary


Check-Cut(I,O,S,Nin,Nout)Pick-Inputs(I,o,O,S,Nin,Nout)Pick-Output(I,O,S,Nin,Nout)

The algorithm works recurively.Input and Output set starts empty and then they grows recursively. For every couple of input and output sets we check if the convex cut generated is valid or not (forbidden nodes)

Poli-Enum-Incr()Pick-Output( , , , Nin , Nout)∅ ∅ ∅


27

Final Result


Example of a convex cut for a particular block


Technology

HPPS 2008 - Panzica Motta Bonanno