Fine grain software pipelining of non-vectorizable nested loops

This paper presents a new technique to parallelize nested loops at the statement level. It transforms sequential nested loops, either vectorizable or not, into parallel ones. Previously, the wavefront method was used to parallelize non-vectorizable nested loops. However, in order to reduce the complexity of parallelization, the wavefront method regards an iteration as an unbreakable scheduling unit and draws parallelism through iteration overlapping. Our technique takes a statement rather than an iteration as the scheduling unit and exploits parallelism by overlapping the statements in all dimensions. In this paper, we show how this finer grain parallelization can be achieved with reasonable computational complexity, and the effectiveness of the resulting method in exploiting parallelism

N-Dimensional Perfect Pipelining

In this paper, we introduce a technique to parallelize nested loops at the fine grain level. It is a generalization of Perfect Pipelining which was developed to parallelize a single-nested loop at the fine grain level. Previous techniques that can parallelize nested loops, e.g. DOACROSS or Wavefront method, mostly belong to the coarse grain approach. We explain our method, contrast it with the coarse grain techniques, and show the benefits of parallelizing nested loops at the fine grain level

A Theoretical Approach Involving Recurrence Resolution, Dependence Cycle Statement Ordering and Subroutine Transformation for the Exploitation of Parallelism in Sequential Code.

To exploit parallelism in Fortran code, this dissertation consists of a study of the following three issues: (1) recurrence resolution in Do-loops for vector processing, (2) dependence cycle statement ordering in Do-loops for parallel processing, and (3) sub-routine parallelization. For recurrence resolution, the major findings include: (1) the node splitting algorithm cannot be used directly to break an essential antidependence link, of which the source variable that results in antidependence is itself the sink variable of another true dependence so a correction method is proposed, (2) a sink variable renaming technique is capable of breaking an antidependence and/or output-dependence link, (3) for recurrences formed by only true dependences, a dynamic dependence concept and the derived technique are powerful, and (4) by integrating related techniques, an algorithm for resolving a general multistatement recurrence is developed. The performance of a parallel loop is determined by the level of parallelism and the time delay due to interprocessor communication and synchronization. For a dependence cycle of a single parallel loop executed in a general synchronization mode, the parallelism exposed varies with the alignment of statements. Statements are reordered on the basis of execution-time of the loop as estimated at compile-time. An improved timing formula and a derived statement ordering algorithm are proposed. Further extension of this algorithm to multiple perfectly nested Do-loops with simple global dependence cycle is also presented. The subroutine is a potential source for parallel processing. Several problems must be solved for subroutine parallelization: (1) the precedence of parallel executions of subroutines, (2) identification of the optimum execution mode for each subroutine and (3) the restructuring of a serial program. A five-step approach to parallelize called subroutines for a calling subroutine is proposed: (1) computation of control dependence, (2) approximation of the global effects of subroutines, (3) analysis of data dependence, (4) identification of execution mode, and (5) restructuring of calling and called subroutines. Application of these five steps in a recursive manner to different levels of calling subroutines in a program addresses the parallelization of subroutines

Loop pipelining with resource and timing constraints

Developing efficient programs for many of the current parallel computers is not easy due to the architectural complexity of those machines. The wide variety of machine organizations often makes it more difficult to port an existing program than to reprogram it completely. Therefore, powerful translators are necessary to generate effective code and free the programmer from concerns about the specific characteristics of the target machine. This work focuses on techniques to be used by an important class of translators, whose objective is to transform sequential programs into equivalent more parallel programs. The transformations are performed at instruction level in order to exploit low level parallelism and increase memory locality.Most of the current applications are programmed in languages which do not allow us to express parallelism between high-level sentences (as Pascal, C or Fortran). Furthermore, a lot of applications written ten or more years ago are still used today, and it is not feasible to rewrite such applications for many reasons (not only technical reasons, but also economic ones). Translators enable programmers to write the application in a familiar sequential programming language, without concerning their selves with the architecture of the target machine. Current compilers for parallel architectures not only translate a program written on a high-level language to the appropriate machine language, but also perform some transformations in the final code in order to execute the program in a more parallel way. The transformations improve the performance in the execution of the program by making use of the knowledge that the compiler has about the machine architecture. The semantics of the program remain intact after any transformation.Experiments show that limiting parallelization to basic blocks not included in loops limits maximum speedup. This is because loops often comprise a large portion of the parallelism available to be exploited in a program. For this reason, a lot of effort has been devoted in the recent years to parallelize loop execution. Several parallel computer architectures and compilation techniques have been proposed to exploit such a parallelism at different granularities. Multiprocessors exploit coarse grained parallelism by distributing entire loop iterations to different processors. Systems oriented to the high-level synthesis (HLS) of VLSI circuits, superscalar processors and very long instruction word (VLIW) processors exploit fine-grained parallelism at instruction level. This work addresses fine-grained parallelization of loops addressed to the HLS of VLSI circuits. Two algorithms are proposed for resource constraints and for timing constraints. An algorithm to reduce the number of registers required to execute a loop in a given architecture is also proposed.Postprint (published version

The exploitation of pipeline parallelism by compile time dataflow analysis

The automatic and implicit transformation of sequential instruction streams, which execute efficiently for pipelined architectures is the subject of this paper. This paper proposes a method which maximizes the parallel performance of an instruction pipeline by detecting and eliminating specific pipeline hazards known as resource conflicts. The detection of resource conflicts is accomplished with data dependence analysis, while the elimination of resource conflicts is accomplished by instruction stream code transformation. The transformation of instruction streams is guided by data dependence analysis, and dependence graphs. This thesis is based on the premise that the elimination of resource conflicts is synonymous with the elimination of specific arcs in the dependence graph. Examples will be given showing how detection and elimination of resource conflicts is possible through compiler optimization

Achieving Superscalar Performance without Superscalar Overheads - A Dataflow Compiler IR for Custom Computing

The difficulty of effectively parallelizing code for multicore processors, combined with the end of threshold voltage scaling has resulted in the problem of \u27Dark Silicon\u27, severely limiting performance scaling despite Moore\u27s Law. To address dark silicon, not only must we drastically improve the energy efficiency of computation, but due to Amdahl\u27s Law, we must do so without compromising sequential performance. Designers increasingly utilize custom hardware to dramatically improve both efficiency and performance in increasingly heterogeneous architectures. Unfortunately, while it efficiently accelerates numeric, data-parallel applications, custom hardware often exhibits poor performance on sequential code, so complex, power-hungry superscalar processors must still be utilized. This paper addresses the problem of improving sequential performance in custom hardware by (a) switching from a statically scheduled to a dynamically scheduled (dataflow) execution model, and (b) developing a new compiler IR for high-level synthesis that enables aggressive exposition of ILP even in the presence of complex control flow. This new IR is directly implemented as a static dataflow graph in hardware by our high-level synthesis tool-chain, and shows an average speedup of 1.13 times over equivalent hardware generated using LegUp, an existing HLS tool. In addition, our new IR allows us to further trade area & energy for performance, increasing the average speedup to 1.55 times, through loop unrolling, with a peak speedup of 4.05 times. Our custom hardware is able to approach the sequential cycle-counts of an Intel Nehalem Core i7 superscalar processor, while consuming on average only 0.25 times the energy of an in-order Altera Nios IIf processor

Transformations of High-Level Synthesis Codes for High-Performance Computing

Specialized hardware architectures promise a major step in performance and energy efficiency over the traditional load/store devices currently employed in large scale computing systems. The adoption of high-level synthesis (HLS) from languages such as C/C++ and OpenCL has greatly increased programmer productivity when designing for such platforms. While this has enabled a wider audience to target specialized hardware, the optimization principles known from traditional software design are no longer sufficient to implement high-performance codes. Fast and efficient codes for reconfigurable platforms are thus still challenging to design. To alleviate this, we present a set of optimizing transformations for HLS, targeting scalable and efficient architectures for high-performance computing (HPC) applications. Our work provides a toolbox for developers, where we systematically identify classes of transformations, the characteristics of their effect on the HLS code and the resulting hardware (e.g., increases data reuse or resource consumption), and the objectives that each transformation can target (e.g., resolve interface contention, or increase parallelism). We show how these can be used to efficiently exploit pipelining, on-chip distributed fast memory, and on-chip streaming dataflow, allowing for massively parallel architectures. To quantify the effect of our transformations, we use them to optimize a set of throughput-oriented FPGA kernels, demonstrating that our enhancements are sufficient to scale up parallelism within the hardware constraints. With the transformations covered, we hope to establish a common framework for performance engineers, compiler developers, and hardware developers, to tap into the performance potential offered by specialized hardware architectures using HLS

Exploiting Outer Loops Vectorization in High Level Synthesis

Synthesis of DoAll loops is a key aspect of High Level Synthesis since they allow to easily exploit the potential parallelism provided by programmable devices. This type of parallelism can be implemented in several ways: by duplicating the implementation of body loop, by exploiting loop pipelining or by applying vectorization. In this paper a methodology for the synthesis of complex DoAll loops based on outer vectorization is proposed. Vectorization is not limited to the innermost loops: complex constructs such as nested loops, conditional constructs and function calls are supported. Experimental results on parallel benchmarks show up to 7.35x speed-up and up to 40 % reduction of area-delay product

Exploiting Vectorization in High Level Synthesis of Nested Irregular Loops

Synthesis of DoAll loops is a key aspect of High Level Synthesis since they allow to easily exploit the potential parallelism provided by programmable devices. This type of parallelism can be implemented in several ways: by duplicating the implementation of body loop, by exploiting loop pipelining or by applying vectorization. In this paper a methodology for the synthesis of nested irregular DoAll loops based on outer vectorization is proposed. The methodology transforms the intermediate representation of the DoAll loop to introduce vectorization and it can be easily integrated in existing state of the art High Level Synthesis flows since does not require any modification in the rest of the flow. Vectorization is not limited to perfectly nested countable loops: conditional constructs and loops with variable number of iterations are supported. Experimental results on parallel benchmarks show that the generated parallel accelerators have significant speed-up with limited penalties in terms of resource usage and frequency decrement