3,226 research outputs found
Survey on Combinatorial Register Allocation and Instruction Scheduling
Register allocation (mapping variables to processor registers or memory) and
instruction scheduling (reordering instructions to increase instruction-level
parallelism) are essential tasks for generating efficient assembly code in a
compiler. In the last three decades, combinatorial optimization has emerged as
an alternative to traditional, heuristic algorithms for these two tasks.
Combinatorial optimization approaches can deliver optimal solutions according
to a model, can precisely capture trade-offs between conflicting decisions, and
are more flexible at the expense of increased compilation time.
This paper provides an exhaustive literature review and a classification of
combinatorial optimization approaches to register allocation and instruction
scheduling, with a focus on the techniques that are most applied in this
context: integer programming, constraint programming, partitioned Boolean
quadratic programming, and enumeration. Researchers in compilers and
combinatorial optimization can benefit from identifying developments, trends,
and challenges in the area; compiler practitioners may discern opportunities
and grasp the potential benefit of applying combinatorial optimization
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Silicon compilation
Silicon compilation is a term used for many different purposes. In this paper we define silicon compilation as a mapping from some higher level description into layout. We define the basic issues in structural and behavioral silicon compilation and some possible solutions to those issues. Finally, we define the concept of an intelligent silicon compiler in which the compiler evaluates the quality of the generated design and attempts to improve it if it is not satisfactory
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Pipelining of register transfer netlists
This paper describes a method for pipelining of register-to-register netlists. We define algorithms for inserting latches in a data path, both inside each unit and between the units as well as between control logic and the data path and for readjusting the state transition table. Experimental results on several benchmarks show 30%-40% improvement in performance
Integer linear programming vs. graph-based methods in code generation
A common characterictic of many applications is that they are aimed at the high-volume consumer market, which is extremely cost-sensitive. However many of them impose stringent performance demands on the underlying system. Therefore the code generation must take into account the restrictions and features given by the target architecture while satisfying these performance demands. High-level language compilers often are unable to generate code meeting these requirements. One reason is the phase coupling problem between instruction scheduling and register allocation. Many compilers perform these tasks separately with each phase ignorant of the require- ments of the other. Commonly, each task is accomplished by using heuristic methods. As the goals of the two phases often conflict, whichever phase is performed first imposes constraints on the other, sometimes producing inefficient code. Integer linear programming (ILP) provides an integrated approach to the combined instruction scheduling and register allocation problem. This way, optimal solutions can be found - albeit at the cost of high compilation times. In our experiments, we considered as target processor the 32-bit DSP ADSP-2106x. We have examined two different ILP formulations and compared them with conventional approaches including list scheduling and the critical path method. Moreover, we have investigated approximations based on the ILP formulations; this way, compilation time can be reduced considerably while still producing near-optimal results. From the results of our implementation, we have concluded that integrating ILP formulations in conventional global algorithms is a promising method for generating high-quality code
SOLUTIONS FOR OPTIMIZING THE DATA PARALLEL PREFIX SUM ALGORITHM USING THE COMPUTE UNIFIED DEVICE ARCHITECTURE
In this paper, we analyze solutions for optimizing the data parallel prefix sum function using the Compute Unified Device Architecture (CUDA) that provides a viable solution for accelerating a broad class of applications. The parallel prefix sum function is an essential building block for many data mining algorithms, and therefore its optimization facilitates the whole data mining process. Finally, we benchmark and evaluate the performance of the optimized parallel prefix sum building block in CUDA.CUDA, threads, GPGPU, parallel prefix sum, parallel processing, task synchronization, warp
On Longest Repeat Queries Using GPU
Repeat finding in strings has important applications in subfields such as
computational biology. The challenge of finding the longest repeats covering
particular string positions was recently proposed and solved by \.{I}leri et
al., using a total of the optimal time and space, where is the
string size. However, their solution can only find the \emph{leftmost} longest
repeat for each of the string position. It is also not known how to
parallelize their solution. In this paper, we propose a new solution for
longest repeat finding, which although is theoretically suboptimal in time but
is conceptually simpler and works faster and uses less memory space in practice
than the optimal solution. Further, our solution can find \emph{all} longest
repeats of every string position, while still maintaining a faster processing
speed and less memory space usage. Moreover, our solution is
\emph{parallelizable} in the shared memory architecture (SMA), enabling it to
take advantage of the modern multi-processor computing platforms such as the
general-purpose graphics processing units (GPU). We have implemented both the
sequential and parallel versions of our solution. Experiments with both
biological and non-biological data show that our sequential and parallel
solutions are faster than the optimal solution by a factor of 2--3.5 and 6--14,
respectively, and use less memory space.Comment: 14 page
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