Efficient performance prediction at the source code level is essential in reducing the turnaround time of software development. In this paper, we introduce a new prediction model, which combines several machine learning algorithms, such as KNN, clustering, similarity, sample and attribute weighting with multiple linear regression techniques, to predict the execution time of Digital Signal Processing (DSP) software at the source code level. Prediction at source code level tends to both under-predict the performance for certain testing samples and over-predict for some other samples. Therefore, we propose a new algorithm called MAX/MIN algorithm to select the best-predicted execution time. To validate the new model, we measure experimentally the execution time of a set of functions selected from PHY DSP Benchmark and run them on TIC64 DSP processor. It is observed that the average absolute relative prediction error is less than 10% between the computed performance from the new model and the actual measured execution time.

Performance prediction is critical in embedded system design for reducing the turnaround time of software. Using simulation to measure the performance of the whole source code is often too slow, particularly after the modification of the source code due to changes in problem specification. In this paper we present a comprehensive method that combines analytical modeling and statistical approach to predicting the performance of application software at source code level. We take samples from EEMBC and SMV benchmarks and gather the static attributes from the source code of those samples as our learning set. To determine the effectiveness of our new approach, we select several functions from PHY Benchmark as our testing set. We then apply multiple linear regression technique enhanced with the inclusion of new approaches by using the popular statistical tool SPSS23 to predict the performance of these functions. Comparing our predicted results with the actual measured values, the outcome is promising as the average relative error is within 20%.

Software pipelining is a loop optimization technique that has been widely implemented in modem optimizing compilers. In order to utilize fully the instruction level parallelism of the recent VLIW DSP processors, DSP programs have to be optimized by software pipelining. However, because of the transformation of the original sequential code, a software-pipelined loop is often difficult to understand, test, and debug. It is also very difficult to reuse and port a software-pipelined loop to other processors, especially when the original sequential code is unavailable. We propose a de-pipelining technique, which converts the optimized assembly code of a software-pipelined loop back to a semantically equivalent sequential counterpart. Preliminary experiments on 20 assembly programs verifies the validity of the proposed de-pipelining algorithm.

This paper proposes the possibility of reuse of the existing optimized DSP code on a scalable high-performance VLIW DSP processor. Since loops are the critical paths in most DSP applications, we focus on issues related to loop optimization. In our approach, we first perform a loop alignment transformation on the source level; we then reuse the existing optimized loop code on the assembly level. The approach is highly portable because it is independent of DSP hardware details. It can be used directly by a DSP programmer on the source level and/or by a DSP compiler designer to implement independent optimization modules.

The performance of current C compilers for DSP is almost unacceptable. One of the most important reasons is the lack of implementing software pipelining. This paper presents a remedy called source-level loop optimization. DSP programmers can use source-level loop optimization first then input its result to the DSP compiler to obtain better assembly code. The implementation of source-level loop optimization is easier than that of software pipelining. The preliminary result with the DSP compiler-challenge C code shows that source-level loop optimization is a portable and efficient approach. The detailed method and working examples are presented.

Modern DSP processors have been integrated with instruction-level parallelism (lLP), which presents a challenge to exploit ILP within DSP applications. Software pipelining is an efficient technique used to expose ILP for loop programs and has been widely used for current microprocessors. It has been also used in DSP compilers, but only for the innermost loops. This paper proposes a new approach which extends software pipelining from innermost loops to whole nested loops in DSP applications. Given a perfect loop, we apply an existing software pipelining approach for the innermost loops, then use the so-called pipelining-dovetailing transformation to extend software pipelining to the outer loops. We also present a transformation to convert a non-perfect nested loop into a perfect one. We have verified the above transformations with some nested loops selected from DSP compiler-challenge C code. The preliminary results are further presented in this paper.

Run-time alias disambiguation (RTD) has been proposed as a technique for pointer aliasing. This paper suggests several RTD approaches which may be used for DOACROSS scheduling to exploit coarse-grained parallelism. We analyze the rerollability problem in the transformation of those RTD approaches to software pipelining in order to exploit the instruction level parallelism available in loops. Finally, we give some suggestion as to how to address the rerollability problem.

This paper studies the problem of simultaneous register allocation and software pipelining. We present the register requirement graph to dynamically reflect the register requirement during software pipelining and develop a Register-Pressure-Sensitive (RPS) scheduling technique. Three algorithms-RPS without spilling, RPS with spilling and software pipelining with a limited number of registers-are proposed. The preliminary experimental results show the efficiency of the three algorithms.

When data dependency graph arcs representing data dependency information are annotated with minimum and maximum timing information, new algorithms are required. Foresighted compaction is a list scheduling technique in which look ahead is used in making decisions. Foresighted compaction is very effective in reducing, failure inherent in greedy compaction algorithms.>

Article Free Access Share on GURPR*: a new global software pipelining algorithm Authors: Bogong Su The College of Staten Island of The City University of New York and Tsinghua University, Beijing, China The College of Staten Island of The City University of New York and Tsinghua University, Beijing, ChinaView Profile , Jian Wang Dept. of Computer Science and Technology, Tsinghua University, Beijing, China Dept. of Computer Science and Technology, Tsinghua University, Beijing, ChinaView Profile Authors Info & Claims MICRO 24: Proceedings of the 24th annual international symposium on MicroarchitectureSeptember 1991 Pages 212–216https://doi.org/10.1145/123465.123509Published:01 September 1991Publication History 38citation233DownloadsMetricsTotal Citations38Total Downloads233Last 12 Months7Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF