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Portability by Automatic Translation: Two Case Studies

Portability by Automatic Translation: Two Case Studies. Yishai A. Feldman The Interdisciplinary Center Herzliya, Israel. First Case: Bogart. Large-Scale Translation from Assembly Language to C Joint Work with Doron A. Friedman. The Problem. 400,000 lines of IBM 370 assembly code

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Portability by Automatic Translation: Two Case Studies

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  1. Portability by Automatic Translation: Two Case Studies Yishai A. Feldman The Interdisciplinary Center Herzliya, Israel

  2. First Case: Bogart Large-Scale Translation from Assembly Language to C Joint Work with Doron A. Friedman

  3. The Problem • 400,000 lines of IBM 370 assembly code • Customers downsizing mainframes • Hand-optimized code over 15 calendar years • Live system

  4. Success Criteria • Portability • Efficiency • Minimum manual work BUT • Readability is not important

  5. Difficult Assembly Features • Registers • Condition code • Untyped language • Unstructured code • Large unstructured memory areas • Portability: • Different byte order • Different word size • Different pointer size

  6. STM R14,R12,12(R13) LR R12,R15 USING HORNER,R12 ST R13,SAV+4 LA R13,SAV LA R7,COEF L R5,0(R7) LA R9,0 LOOP CR R9,R2 BNL OUT LA R9,1(R9) LA R7,4(R7) MR R4,R3 A R5,0(R7) B LOOP OUT LR R0,R5 LM R1,R12,24(R13) BR R14 void HORNER(tagSAPReg *Reg) { T_stm(14,12,((Reg[13].ucp+12)),Reg); Reg[12].sw = Reg[15].sw ; SAV[1] = Reg[13].sw ; Reg[13].pv = &(SAV[0]) ; Reg[7].pv = &(COEF[0]) ; Reg[5].sw = *(sWord *)Reg[7].ucp; Reg[9].sw = 0 ; LOOP: if ((Reg[9].sw) >= Reg[2].sw) goto OUT; Reg[9].sw += 1; Reg[7].sw += 4; T_mult(&Reg[4],Reg[3].sw) ; Reg[5].sw += *((sWord *)(Reg[7].ucp)); goto LOOP ; OUT: Reg[0].sw = Reg[5].sw; T_lm(1,12,((Reg[13].ucp+24)),Reg); return; } The Simulating Translator

  7. BogartBetterOptimizing General-purpose AbstractRepresentation Translator

  8. Translation byAbstraction, Transformation, and Reimplementation • Control-flow and data-flow analysis • Typing by constraint propagation • Automatic cliche recognition Abstraction Transformation Re-implementation

  9. void HORNER(tagSAPReg *Reg) { T_stm(14,12,((Reg[13].ucp+12)),Reg); Reg[12].sw = Reg[15].sw ; SAV[1] = Reg[13].sw ; Reg[13].pv = &(SAV[0]) ; Reg[7].pv = &(COEF[0]) ; Reg[5].sw = *(sWord *)Reg[7].ucp; Reg[9].sw = 0 ; LOOP: if ((Reg[9].sw) >= Reg[2].sw) goto OUT; Reg[9].sw += 1; Reg[7].sw += 4; T_mult(&Reg[4],Reg[3].sw) ; Reg[5].sw += *((sWord *)(Reg[7].ucp)); goto LOOP ; OUT: Reg[0].sw = Reg[5].sw; T_lm(1,12,((Reg[13].ucp+24)),Reg); return; } sWord HORNER(sWord r2sw, sWord r3sw) { sWord r5sw; sWordPtr r7swp; sWord r9sw; r7swp = (sWord *)(&COEF[0]); r5sw = *r7swp; r9sw = 0; while (r9sw < r2sw) { r9sw++; r7swp++; r5sw = r5sw*r3sw + *r7swp; } return r5sw; } The Code Produced by Bogart

  10. Accumulating Compound Expressions Assembly Simulating translator L R11,NIG L R5,0(R4) BAL R14,INCTAB LPR R7,R0 MR R2,R7 LR R0,R3 BR R14 Reg[11].sw = NIG; Reg[5].sw = *(sWord *)Reg[4].ucp; INCTAB(Reg); Reg[7].sw = labs(Reg[0].sw); T_mult(&Reg[2], Reg[7].sw); Reg[0].sw = Reg[3].sw; return; return r3sw * labs(INCTAB(NIG,(*r4swp))); Bogart

  11. Example: Condition Code Support Assembly Bogart CGLOOP CH R7,0(R5,R4) SRL R2,1 BH CGADD BNH CGSUB r2sh >>= 1; temp = r4ucp + r5sh; if (r7sh > temp) goto CGADD; if (r7sh <= temp) goto CGSUB; Simulating translator if (Reg[7].sw == *((sHalf *)((Reg[4].ucp+Reg[5].sw)))) __CC = _CZero; else if (Reg[7].sw < *((sHalf *)((Reg[4].ucp+Reg[5].sw)))) __CC = _COne; else __CC = _CTwo; Reg[2].uw >>= 1; if (__CC & 0x4) goto CGADD; if (__CC & 0x3) goto CGSUB;

  12. The Plan Representation CGR CH R2,GMF BL CONGR SRL R2,1 B CGR CONGR ....

  13. Global Type Analysis by Constraint Propagation

  14. Time and Space ComparisonBogart and Simulating Translator Simulator Bogart Time (sec.) Space (bytes) Time (sec.) Space (bytes) 63 4170 33 2802 BIN HORNER 10 3302 3 2465 RANDOM 9 5447 4 2741 SAPDBMS 18 41700 9 29073 (SAPDBMS is a central Sapiens module)

  15. Time Performance on Several Platforms(For example routine BIN) IBM 370 RS/6000 AS/400 PC (DOS) Original Assembly 0.32 1.74 1.91 failed 1.26 Simulator 1 1 1 1 Bogart Hand Crafted 0.91 0.97 0.49 1.07

  16. Results • Bogart produces more portable code • Bogart supports a larger portion of the source language • Bogart requires less manual work in code preparation • Bogart produces more efficient code in terms of time and space performance

  17. Conclusions • Translation by abstraction produces better results than simulation on all criteria • Simulation is simpler and faster to implement • Simulated code is easier for the programmers to debug • The advantages of the abstraction approach grow in the long term • “Research-then-transfer” versus “Industry-as-laboratory” (Colin Potts, 1993)

  18. New Developments:Bogart  Falcon 2000

  19. Second Case: MIDAS Automatic High-Quality Reengineering of Database Programs by Temporal Abstraction Joint Work with Yossi Cohen

  20. The Problem Legacy Database Software • Much legacy software is DP, many database-related programs • Conversion from older models (indexed-sequential, hierarchical, network) to relational/object-oriented databases • Need to convert: • Schema • Data • Software

  21. Network vs. Relational Databases

  22. Network Database Program (1) 01 MOVE 0 TO STATUS1. 02 PERFORM UNTIL STATUS1 IS NOT EQUAL TO ZERO 03FETCH NEXT STUDENT WITHIN DEPT-OF-STUDENT 04 AT END MOVE 1 TO STATUS1 05 IF STATUS1 IS EQUAL TO 0 THEN 06IF STUDENT-DEGREE IS EQUAL TO 2 THEN 07MOVE 0 TO GRADES-SUM 08 MOVE 0 TO GRADES-COUNT 09 PERFORM SUM-STUDENT-GRADES 10 DIVIDE GRADES-SUM BY GRADES-COUNT 11 GIVING GRADES-AVG 12IF GRADES-AVG > 95THEN 13 DISPLAY ..., GRADES-AVG 14 END-IF 15 END-IF 16 END-IF 17 END-PERFORM.

  23. Network Database Program (2) 18 SUM-STUDENT-GRADES. 19 MOVE 0 TO STATUS2 20 PERFORM UNTIL STATUS2 IS NOT EQUAL TO ZERO 21FETCH NEXT GRADES WITHIN STUDENT-OF-GRADES 22 AT END MOVE 1 TO STATUS2 23 IF STATUS2 IS EQUAL TO 0 THEN 24ADD GRD-GRADE TO GRADES-SUM 25 ADD 1 TO GRADES-COUNT 26 END-IF 27 END-PERFORM.

  24. Naive Translation (1) 01 EXEC SQL DECLARE CRS1 CURSOR FOR 02SELECT ... FROM STUDENT 03 WHERE DEPT-NAME = :DEPT-NAME 04 END-EXEC. 05 EXEC SQL DECLARE CRS2 CURSOR FOR 06SELECT ... FROM GRADES 07 WHERE STUDENT-ID = :STUDENT-ID 08 END-EXEC.

  25. Naive Translation (2) 09 MOVE 0 TO STATUS1 10 EXEC SQL OPEN CRS1 END-EXEC 11 PERFORM UNTIL STATUS1 IS NOT EQUAL TO 0 12EXEC SQL FETCH CRS1 INTO ... END-EXEC. 13 IF SQL-STATUS = SQL-NOT-FOUND THEN MOVE 1 TO STATUS1. 14 IF STATUS1 IS EQUAL TO 0 THEN 15IF STUDENT-DEGREE IS EQUAL TO 2 THEN 16MOVE 0 TO GRADES-SUM 17 MOVE 0 TO GRADES-COUNT 18 PERFORM SUM-STUDENT-GRADES 19 DIVIDE GRADES-SUM INTO GRADES-COUNT GIVING GRADES-AVG 20IF GRADES-AVG > 95 THEN 21 DISPLAY ..., GRADES-AVG 22 END-IF 23 END-IF 24 END-IF 25 END-PERFORM. 26 EXEC SQL CLOSE CRS1 END-EXEC.

  26. Naive Translation (3) 27 SUM-STUDENT-GRADES. 28 MOVE 0 TO STATUS2 29 EXEC SQL OPEN CRS2 END-EXEC. 30 PERFORM UNTIL STATUS2 IS NOT EQUAL TO 0 31EXEC SQL FETCH CRS2 INTO ... END-EXEC. 32 IF SQL-STATUS = SQL-NOT-FOUND 33 THEN MOVE 1 TO STATUS2. 34 IF STATUS2 IS EQUAL TO 0 THEN 35ADD GRD-GRADE TO GRADES-SUM 36 ADD 1 TO GRADES-COUNT 37 END-IF 38 END-PERFORM. 39 EXEC SQL CLOSE CRS2 END-EXEC.

  27. MIDAS: Translation by Abstraction, Transformation, and Re-implementation Plan Temporal Plan Temporal Abstraction Abstraction Re-implementation Network DB code Relational DB code

  28. MIDAS Translation 01 EXEC SQL DECLARE CRS1 CURSOR FOR 02 SELECT STUDENT.STUDENT-ID, FIRST-NAME, LAST-NAME, AVG(GRADE) 03 FROM STUDENT, GRADES 04 WHERE DEGREE = 2 05 AND DEPT-NAME = :DEPT-NAME 06 AND GRADES.STUDENT-ID = STUDENT.STUDENT-ID 07GROUP BY STUDENT.STUDENT-ID, FIRST-NAME, LAST-NAME 08HAVING AVG(GRADE) > 95 09 END-EXEC. 10 PERFORM UNTIL SQL-STATUS = SQL-NOT-FOUND 11 EXEC SQL FETCH CRS1 INTO ... END-EXEC. 12 DISPLAY ..., GRADES-AVG 13 END-PERFORM

  29. The Internal Representation:Query Graphs • Temporal abstraction • Generate / Join • Filter • Map • Aggregate • Wide-spectrum formalism

  30. Filtering Transformation

  31. Join-Down Transformation

  32. Accumulation Transformation

  33. Performance Results

  34. Conclusions • Translation by abstraction, transformation, and re-implementation demonstrated in two domains • Query graphs as abstraction for database operations • Adapts to different schema transformations • Scalability

  35. Conclusions and Future Work • Appropriate domain • Same host language • Few cliches give wide coverage • Important commercially • Generalizations • Other legacy models • OODB / 4GL as targets

  36. Questions? Papers can be downloaded from http://www.idc.ac.il/yishai

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