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PROGRAM 20.4: (a) A for-loop to be software-pipelined. (b) After a scalar-replacement optimization (in the deFlnition of a); and scalar variables labeled with their iteration-number. GRAPH 20.5: Data-dependence graph for Program 20.4b: (a) original graph, in which solid edges are same-iteration dependences and dotted edges are loop-carried dependences; (b) acyclic dependences of the unrolled loop. ALGORITHM 20.9: Iterative modulo scheduling. Chapter 21: The Memory Hierarchy PROGRAM 21.6: Inserting prefetches using loop unrolling or nested loops. Appendix A: MiniJava Language Reference Manual GRAMMAR A.2 Team-Fly
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PROGRAM 15.15: Summing the squares. PROGRAM 15.16: Partial call-by-name using the results of strictness analysis; compare with Program 15.14. ALGORITHM 15.17: First-order strictness analysis. Chapter 16: Polymorphic Types ALGORITHM 16.1: Checking wellformedness of types and subtyping. ALGORITHM 16.2: Field and method search. ALGORITHM 16.3: Type-checking expressions. Expressions with integer type are omitted, because they are checked just as in MiniJava ALGORITHM 16.4: Type-checking class declarations. Chapter 17: Dataflow Analysis PROGRAM 17.3 ALGORITHM 17.5: Topological sort by depth-first search. ALGORITHM 17.6: A work-list algorithm for reaching definitions. ALGORITHM 17.7: Value numbering. PROGRAM 17.9: p and q are not aliases. Chapter 18: Loop Optimizations PROGRAM 18.8: A loop before and after induction-variable optimizations. PROGRAM 18.10: Useless loop unrolling. PROGRAM 18.11: Useful loop unrolling; (a) works correctly only for an even number of iterations of the original loop; (b) works for any number of iterations of the original loop. Chapter 19: Static Single-Assignment Form ALGORITHM 19.6: Inserting -functions. ALGORITHM 19.7: Renaming variables. ALGORITHM 19.9: Lengauer-Tarjan algorithm for computing dominators. ALGORITHM 19.10: Two versions of AncestorWithLowestSemi and Link functions for operations on spanning forest. The naive version (a) takes O(N) per operation (so the algorithm runs in time O(N2)) and the efficient version (b) takes O(log N) amortized time per operation, for an O(N log N) algorithm. ALGORITHM 19.12: Dead-code elimination in SSA form. ALGORITHM 19.17: Calculation of live ranges in SSA form, and building the inter-ference graph. The graph-walking algorithm is expressed as a mutual recursion between LiveOutAtBlock, LiveInAtStatement, and LiveOutAtStatement. The recursion is bounded whenever LiveOutAtBlock finds an already walked block, or whenever LiveOutAtStatement reaches the definition of v. PROGRAM 19.19: SSA program of Figure 19.4g converted to functional intermediate form. ALGORITHM 19.20: Translating SSA to functional intermediate form. Chapter 20: Pipelining and Scheduling
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move instructions. GRAPH 11.2: After removal of h, g, k. GRAPH 11.5: (a) after coalescing c and d; (b) after coalescing b and j. PROGRAM 11.8: A C function and its translation into instructions ALGORITHM 11.9: Simple register allocation on trees. ALGORITHM 11.10: Sethi-Ullman labeling algorithm. ALGORITHM 11.11: Sethi-Ullman register allocation for trees. Chapter 12: Putting It All Together PROGRAM 12.1: Package Frame. Chapter 13: Garbage Collection ALGORITHM 13.2: Depth-first search. ALGORITHM 13.3: Mark-and-sweep garbage collection. ALGORITHM 13.5: Depth-first search using an explicit stack. ALGORITHM 13.6: Depth-first search using pointer reversal. ALGORITHM 13.8: Forwarding a pointer. ALGORITHM 13.9: Breadth-first copying garbage collection. ALGORITHM 13.11: Semi-depth-first forwarding. ALGORITHM 13.13: Basic tricolor marking Chapter 14: Object-Oriented Languages PROGRAM 14.1: An object-oriented program. Chapter 15: Functional Programming Languages PROGRAM 15.1: A FunJava program. PROGRAM 15.3: Binary search trees implemented in two ways. PROGRAM 15.4: Built-in types and functions for PureFunJava. PROGRAM 15.5: PureFunJava program to read i, print i!. PROGRAM 15.6: printTable in PureFunJava. PROGRAM 15.7: Java implementation of printTable. ALGORITHM 15.8: Inline expansion of function bodies. We assume that no two declarations declare the same name. ALGORITHM 15.9: Loop-preheader transformation. ALGORITHM 15.10: Loop-invariant hoisting. PROGRAM 15.11: printTable as automatically specialized. PROGRAM 15.12: printTable after closure conversion (class constructors omitted). PROGRAM 15.14: Call-by-name transformation applied to Program 15.3a.
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PROGRAM 4.5: Exp class for Program 4.4. PROGRAM 4.7: Syntax classes with accept methods. PROGRAM 4.8: An interpreter visitor. PROGRAM 4.10: MiniJava visitor Chapter 5: Semantic Analysis PROGRAM 5.2: Hash table with external chaining. PROGRAM 5.5: The interface of package Symbol. PROGRAM 5.6: Symbol table implementation. PROGRAM 5.8: A visit method for variable declarations PROGRAM 5.9: A visit method for plus expressions Chapter 6: Activation Records PROGRAM 6.1: An example of higher-order functions. PROGRAM 6.3: Nested functions. Chapter 7: Translation to Intermediate Code PROGRAM 7.3: The Cx class. PROGRAM 7.5: A MiniJava program. Chapter 8: Basic Blocks and Traces ALGORITHM 8.2: Generation of traces. Chapter 9: Instruction Selection PROGRAM 9.3: Maximal Munch in Java. PROGRAM 9.5: Assem-instructions for munchStm. PROGRAM 9.6: Assem-instructions for munchExp. PROGRAM 9.7: The Codegen class. Chapter 10: Liveness Analysis GRAPH 10.1: Control-flow graph of a program. EQUATIONS 10.3: Dataflow equations for liveness analysis. ALGORITHM 10.4: Computation of liveness by iteration. GRAPH 10.8: Standard static dataflow analysis will not take advantage of the fact that node 4 can never be reached. PROGRAM 10.10: The Graph abstract data type. Chapter 11: Register Allocation GRAPH 11.1: Interference graph for a program. Dotted lines are not interference edges but indicate
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Team-Fly List of Examples Chapter 1: Introduction GRAMMAR 1.3: A straight-line programming language. PROGRAM 1.5: Representation of straight-line programs. Chapter 2: Lexical Analysis PROGRAM 2.9: JavaCC specification of the tokens from Figure 2.2. PROGRAM 2.10: SableCC specification of the tokens from Figure 2.2. Chapter 3: Parsing GRAMMAR 3.1: A syntax for straight-line programs. DERIVATION 3.2 GRAMMAR 3.5 GRAMMAR 3.8 GRAMMAR 3.10 GRAMMAR 3.11 GRAMMAR 3.12 ALGORITHM 3.13: Iterative computation of FIRST, FOLLOW, and nullable. GRAMMAR 3.15 GRAMMAR 3.20 GRAMMAR 3.23 GRAMMAR 3.26: A grammar capturing the essence of expressions, variables, and pointer dereference (by the *) operator in the C language. GRAMMAR 3.30 GRAMMAR 3.31: JavaCC version of Grammar 3.30. GRAMMAR 3.32: SableCC version of Grammar 3.30. GRAMMAR 3.34: SableCC productions of Grammar 3.32 with conflicts resolved. GRAMMAR 3.37: Yacc grammar with precedence directives. GRAMMAR 3.38: Yacc grammar with precedence directives. Chapter 4: Abstract Syntax PROGRAM 4.1: Recursive-descent interpreter for part of Grammar 3.15. PROGRAM 4.2: JavaCC version of a variant of Grammar 3.15. GRAMMAR 4.3: Abstract syntax of expressions. PROGRAM 4.4: Building syntax trees for expressions.
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Team-Fly
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Team-Fly List of Tables Chapter 1: Introduction Table 1.2: Description of compiler phases. Chapter 3: Parsing Table 3.16: Nullable, FIRST, and FOLLOW for Grammar 3.15. Table 3.17: Predictive parsing table for Grammar 3.15. We omit the columns for num, /, and -, as they are similar to others in the table. Table 3.19: LR parsing table for Grammar 3.1. Table 3.22: LR(0) parsing table for Grammar 3.20. Table 3.28: LR(1) and LALR(1) parsing tables for Grammar 3.26. Table 3.35: LR parsing table for Grammar 3.5. Table 3.36: Conflicts of Table 3.35 resolved. Chapter 6: Activation Records Table 6.4: Formal parameters for g(x1, x2, x3) where x1 escapes. Chapter 10: Liveness Analysis Table 10.5: Liveness calculation following forward control-flow edges. Table 10.6: Liveness calculation following reverse control-flow edges. Table 10.7: X and Y are solutions to the liveness equations; Z is not a solution. Chapter 14: Object-Oriented Languages Table 14.6. Facilities for type testing and safe casting. Chapter 17: Dataflow Analysis Table 17.1: Quadruples expressed in the Tree language. Occurrences of a, b, c, f, L denote TEMP, CONST, or LABEL nodes only. Table 17.2: Gen and kill for reaching definitions. Table 17.4: Gen and kill for available expressions. Table 17.10: Transfer function for alias flow analysis. Chapter 20: Pipelining and Scheduling Table 20.6: (a) Tableau of software-pipelined loop schedule; there is a group of instructions fj with slope 0, another group abg with slope 2, and a third group cdeh with slope 3. (b) The smaller-slope groups are pushed down to slope 3, and a pattern is found (boxed) that constitutes the pipelined loop.
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Chapter 21: The Memory Hierarchy Figure 21.1: Organization of a direct-mapped cache. Key field of the address is used to index the tags array and the data blocks; if tags[key] matches the tag field of the address then the data is valid (cache hit). Word index is used to select a word from the cache block. Figure 21.2: The memory hierarchy. Figure 21.3: Alignment of data objects (or basic blocks) to avoid crossing cache-block boundaries is often worthwhile, even at the cost of empty space between objects. Figure 21.4: If x is rarely true, basic-block placement (a) will occupy three in-cache blocks, while (b) will usually occupy only two. Figure 21.5: Execution of a dot-product loop, with 4-word cache blocks. (a) Without prefetching, on a machine with dynamic instruction reordering, the number of outstanding instructions (reserved registers) grows proportionally to the cache-miss latency. (b) With prefetching, the hardware reservation table never grows large. (Steady-state behavior is shown here, not the initial transient.) Figure 21.7: Matrix multiplication. Each element of C is computed from a row of A and a column of B. With blocking, a c c block of the C matrix is computed from a c N block of A and a N c block of B. Team-Fly
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form. a0, b0, c0 are initial values of the variables before block 1. Figure 19.4: Conversion of a program to static single-assignment form. Node 7 is a postbody node, inserted to make sure there is only one loop edge (see Exercise 18.6); such nodes are not strictly necessary but are sometimes helpful. Figure 19.5: Node 5 dominates all the nodes in the grey area. (a) Dominance frontier of node 5 includes the nodes (4, 5, 12, 13) that are targets of edges crossing from the region dominated by 5 (grey area including node 5) to the region not strictly dominated by 5 (white area including node 5). (b) Any node in the dominance frontier of n is also a point of convergence of nonintersecting paths, one from n and one from the root node. (c) Another example of converging paths P1,5 and P5,5. Figure 19.8: A control-Flow graph and trees derived from it. The numeric labels in part (b) are the dfnums of the nodes. Figure 19.11: Path compression. (a) Ancestor links in a spanning tree; AncestorWithLowestSemi(v) traverses three links. (b) New nodes a2, a3 are linked into the tree. Now AncestorWithLowestSemi(w) would traverse 6 links. (c) AncestorWithLowestSemi(v) with path compression redirects ancestor links, but best[v] remembers the best intervening node on the compressed path between v and a1. (d) Now, after a2 and a3 are linked, AncestorWithLowestSemi(w) traverses only 4 links. Figure 19.13: Conditional constant propagation. Figure 19.14: This transformation does not preserve the dominance property of SSA form, and should be avoided. Figure 19.15: Construction of the control-dependence graph. Figure 19.16: Aggressive dead-code elimination Figure 19.18: Functional intermediate representation. Binding occurrences of variables are underlined. Chapter 20: Pipelining and Scheduling Figure 20.1: Functional unit requirements of instructions (on the MIPS R4000 processor). This machine’s Floating-point ADD instruction uses the instruction-fetch unit for one cycle; reads registers for one cycle; unpacks exponent and mantissa; then for the next cycle uses a shifter and an adder; then uses both the adder and a rounding unit; then the rounding unit and a shifter; then writes a result back to the register file. The MULT and CONV instructions use functional units in a different order. Figure 20.2: If there is only one functional unit of each kind, then an ADD cannot be started at the same time as a MULT (because of numerous resource hazards shown in boldface); nor three cycles after the MULT (because of Add, Round, and Write hazards); nor four cycles later (because of Add and Round hazards). But if there were two adders and two rounding units, then an ADD could be started four cycles after a MULT. Or with dual fetch units, multiple-access register file, and dual unpackers, the MULT and ADD could be started simultaneously. Figure 20.3: Data dependence. (Above) If the MULT produces a result that is an operand to ADD, the MULT must write its result to the register file before the ADD can read it. (Below) Special bypassing circuitry can route the result of MULT directly to the Shift and Add units, skipping the Write, Read, and Unpack stages. Figure 20.7: Pipelined schedule. Assignments in each row happen simultaneously; each right-hand side refers to the value before the assignment. The loop exit test i < N + 1 has been "moved past" three increments of i, so appears as i < N - 2. Figure 20.8: Pipelined schedule, with move instructions. Figure 20.10: Iterative modulo scheduling applied to Program 20.4b. Graph 20.5a is the data- dependence graph; min = 5 (see page 451); H =[c, d, e, a, b, f, j, g, h]. Figure 20.11: Dependence of ADD's instruction-fetch on result of BRANCH.
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Figure 13.1: A heap to be garbage collected. Class descriptors are not shown in the diagram. Figure 13.4: Mark-and-sweep collection. Figure 13.7: Copying collection. Figure 13.10: Breadth-first copying collection. Figure 13.12: Generational collection. The bold arrow is one of the rare pointers from an older generation to a newer one. Chapter 14: Object-Oriented Languages Figure 14.2: Single inheritance of data fields. Figure 14.3: Class descriptors for dynamic method lookup. Figure 14.4: Multiple inheritance of data fields. Figure 14.5: Field offsets in descriptors for multiple inheritance. Chapter 15: Functional Programming Languages Figure 15.2: Closures for execution of twice(add(5)). SL=static link; RV=return value; EP=escaping-variables-pointer or environment-pointer. Figure 15.13: printTable as compiled. Chapter 17: Dataflow Analysis Figure 17.8: An illustration of value numbering. (a) A basic block; (b) the table created by the value- numbering algorithm, with hidden bindings shown crossed out; (c) a view of the table as a DAG. Chapter 18: Loop Optimizations Figure 18.1: Some loops; in each case, 1 is the header node. Figure 18.2: None of these contains a loop. Dotted lines indicate reduction of graph (c) by deleting edges and collapsing nodes. Figure 18.3: (a) A flow graph; (b) its dominator tree. Figure 18.4: The loop-nest tree for Figure 18.3a. Each loop header is shown in the top half of each oval (nodes 1, 2, 5, ; a loop comprises a header node (e.g., node 5), all the other nodes shown in the same oval (e.g., node 10), and all the nodes shown in subtrees of the loop-nest-tree node (e.g., 8, 9). Figure 18.5: (a) A loop; (b) the same loop with a preheader. Figure 18.6: Some good and bad candidates for hoisting t . a . b. Figure 18.7: A while loop (a), transformed into a repeat loop (b). Figure 18.9: The basic induction variable i is incremented by different amounts in different iterations; the derived induction variable j is not changed in every iteration. Chapter 19: Static Single-Assignment Form Figure 19.1: (a) A straight-line program. (b) The program in singleassignment form. Figure 19.2: (a) A program with a control-Flow join; (b) the program trans formed to single assignment form; (c) edge-split SSA form. Figure 19.3: (a) A program with a loop; (b) the program transformed to edgesplit single-assignment
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