Thread-level speculation (TLS) [1,2,3,4], which is also called Speculative Multithreading (SpMT for short) and makes use of parallelizing compiler [5] is an auto-parallelization technique on multi-core processors for sequential programs. Without user interactions and with lowest cost, TLS can accelerate irregular sequential programs with comparison to manual parallelization. The products of\(\hbox {TLS}^{'}\)s development are shown in Table 1.

Since instruction-level parallelism seems to provide diminishing speedup, SpMT has become a more attractive choice, especially suitable for current chip multiprocessor(CMP) architecture [6]. CMP is a simple, economical, low power, and high-performance architecture, offering more computing and storage resources, increasing communication bandwidth and reducing communication latency. To improve speedup of irregular sequential programs on CMP, a great number of the sequential application programs must be reconstructed so that they can be executed automatically in parallel [7]. Thread-level speculation (TLS), also known as SpMT, is an automatic parallelization of sequential programs on multi-core processors by parallelizing compiler technology [5]. Compared with manual parallelization, TLS can accelerate irregular sequential programs with lowest cost and without user interactions. Examples of TLS include Multiscalar [8], SEED [9], DOE [10], DOMORE [11], Hydra [12], Pinot [13], POSH [14] and Mitosis [15].

Compiler-based automatic parallelization is a widely studied area and speculative parallelization can potentially deliver significant speedups for sequential programs. Loops have regular structures and significant coverage on execution time, so loops are ideal candidates for extracting parallel threads. The studies [9,11,16,17,18] focus on loops, and they decompose loops into multiple code segments to achieve the performance improvement. These approaches have focused on loops. However, non-loop code sections are also crucial to the irregular application programs [19]. These researches [15,20,21,22] partition the whole program into multiple threads to be executed in parallel. Current automatic parallelization technique is still in maturing. These researches above use heuristic-based thread partitioning approach to select speculative threads from top to bottom and only estimate the speculative multithreaded execution performance indirectly. These heuristics-based methods yield some performance improvements, but heuristic-based thread partitioning approaches apply the same partitioning strategies to partitioning all application programs with different program characteristics. They are one-size-fits-all strategies and suitable for one kind of programs while our approach based on machine learning proposes a program-aware partitioning scheme according to characteristics of the target program.

In recent years, the researches [1,36,37,38,39,40,41,42,43] introduced various machine learning approaches to the parallelism of programs. Wang et al. [40] made use of a machine learning method to map a parallelized program to multi-core processors with an automatic compiler-based approach. They aimed to choose the satisfied number of threads for the scheduling strategy and a parallel program. For a given loop, Chen et al. [41] selected a suitable version from representative multi-threaded versions, which were adaptively generated by an OpenMP-based mechanism, and executed with one machine learning method on an architecture of multi-core processor. Long et al. [38] allocated parallel workloads with a cost-aware manner by a learning-based approach. Before determining the best workload allocation scheme, they classified programs with static program features on the foundation of its prior experience with similar programs. The paper [36] shew a novel graph-based thread partition approach, firstly to use graph to characterize programs, including data and control information, as well as static and dynamic features; secondly to learn partition knowledge as well as to predict partition for unseen programs. In short, the characteristics of previous studies can be classified into two headings: firstly, the procedures in one program use the same partition scheme; second, the final partition unit is one program, rather than a procedure. The paper [44] proposed a Graph Neural Networks for Multiple GPUs. In order to ensure model accuracy, training was conducted based on the full graph, and different segmentation schemes for multi GPU were explored. The impact of different graph data layouts on GPU performance during graph neural network computation was studied, and a sparse block aware GPU memory access optimization technique was proposed.

Different from previous works, this paper proposes an importance degree based thread partitioning approach (IDaTPA), to implement biasing partitioning on procedures in one program according to their importance degrees. The core of IDaTPA is a prediction model, which makes use of a machine learning method to learn partitioning knowledge and to predict partitioning schemes for unknown programs. IDaTPA faces two main difficulties: firstly, irregular programs are not sufficient for training leaning model; secondly, it is a more difficult task to obtain irregular\(\hbox {programs}^{'}\) best thread partition structures for irregular programs.

This paper makes the following contributions:

• To address the generation problem of sample set, we first of all generate samples automatically by heuristic rules, which are mips codes consisting of control quasi-independent points (CQIPs) and spawning points (SPs); secondly manually optimize the samples by adjusting the positions of SPs and CQIPs as well as rebuilding precomputation-slice (p-slice), so to gain better performance for every sample; then make a model building to guarantee that the probability of adjusting to the satisfied partition positions is increasing;

• To obtain the satisfied partition structures of irregular programs as training samples, we first partition these irregular programs repeatedly on the Prophet simulator with the HR-based partition approach and select training samples from the satisfied partition structures for programs. Every training sample consists of the program’s feature vector of a program and its corresponding satisfied partition structure, which can yield the maximum speedup on multi-core processors;

• We build a prediction model automatically with adequate training samples, which imply prior partition knowledge. With this model, the best partition structure for an unseen program is predicted to guide thread partition when a program need be dealt with.

The validity of IDaTPA is demonstrated through executing Olden and SPEC2020 benchmarks on the Prophet simulator [27], and IDaTPA gets speedups from 1.33 to 2.68 with an average value of 1.80. Compared to the conventional HR-based approach, IDaTPA gains a 5.75% performance improvement. Furthermore, on 8-core processor, IDaTPA obtains a 66.23% performance improvement, validating the portability of IDaTPA.

The remaining parts of this paper are organized in the followings: Sect. 2 presents a brief description of the SpMT execution model and the HR-based thread partitioning approach; the framework of IDaTPA is presented in Sect. 3; based on the framework, we present expression of partition knowledge, including generation of hybrid sample set, expression of knowledge in Sect. 4. Section 5 describes similarity comparison as well as implementation of IDaTPA, including building of a prediction model, predicting partitioned schemes for an unseen irregular program; and Sect. 6 shows the experimental results and analysis; finally, conclusion and perspectives are present in Sect. 7.

2.1Execution model

As an effective mechanism, TLS parallelizes irregular programs to realize the improvement of speedups. With an execution model of TLS, sequential programs are performed partition into respective multiple threads, each of which does execution of every part in the sequential programs. Among all of concurrently executed threads, non-speculative thread is a special thread which is the only one thread allowed to commit results to the shared memory. The other threads are all speculative thread. A spawning instruction pair marks the speculative thread. On program execution, when a spawning instruction is recognized and if spawning is allowed by the existing processor resources, a new speculative thread is to be spawned by its parent thread. After the non-speculative thread finishes execution, it need verify whether or not the generated data of its successor thread is correct. If the validation proves to be true, all generated values will be committed to memory by the non-speculative thread to memory, then the successor thread turns to be non-speculative. If not, the non-speculative threads would make all speculative successor threads revoked and re-executed.

On Prophet, SP and CQIP constitute the spawning instruction pair. During program execution, a new thread which executes the code segment following the CQIP speculatively is generated from the SP in a parent thread. Figure 1 shows the execution model of TLS. A speculative multithreaded program is generated by mapping sequential programs with SPs and CQIPs. If SPs and CQIPs are ignored, the speculative multithreaded program turns to be one sequential program, which is illustrated in Fig. 1a. When there appears one SP in parent thread on program execution and there exist idle cores, a new speculative thread will be spawned and the code segment following the CQIP will be executed speculatively, and Fig. 1b shows the process. What leads to speculation failure is Read after Write (RAW) violations or validation failure. When validation failure happens, a parent thread makes the speculative threads executed in a sequential manner, and the process is shown in Fig. 1c. Figure 1d shows that there is a RAW dependence violation, in which the speculative thread restarts itself upon its current state.

Models for TLS.a Sequential execution.b Successful parallel execution.c Failed parallel execution.d Read-after-write violation

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2.2Thread partition

Thread partition refers to that one sequential program is partitioned by compiler into multiple speculative threads, in order to yield better performance improvement. To yield the satisfied speedup, thread partition need take thread size, data dependence, inter-thread control dependence and other factors into account to make a program partitioned into the best thread combination. In general, it is a NP-complete problem [45] to find a satisfied thread partitioning, so heuristic rules-based (HR-based) thread partition is a very important approach.

Prophet [26], which considers the need of partitioning algorithm, firstly designs and develops a profiler to capture the programs’ runtime information, such as probabilities of branches, dynamic instruction counts of loops and functions. Secondly, the dynamic information is added by corresponding control flow graph (CFG for short). Based on CFG, loop regions and non-loop regions are successively partitioned. During thread partitioning, certain heuristic rules are made use of to select SP-CQIPs in the CFGs.

• In programs, SP can exist anywhere, and is behind function call instruction as far as possible. In non-loop region, CQIP is set in the entrance of basic block while in loop region CQIP is located in front of the loop branch instruction which is in the last basic block of the loop.

• SPs and CQIPs are located within the same loop or the same function. The maximum of dynamic instructions from SP to CQIP should be THREAD_UPPER_LIMIT, while the minimum is THREAD_LOWER_LIMIT.

• The minimum of spawning distance between candidate threads is DIS_LOWER_LIMIT.

• DDC for two consecutive candidate threads should not be more than DEP_THRESHOLD.

2.3Evaluation of performance

Usually, assume the execution time for a parallelized program uponN cores, and for the original sequential program is respectively\(T_{p}\),\(T_{s}\), the formula (1) defines the absolute speedup.

This section primarily describes the framework of IDaTPA, including basic idea and construction of predictor. The following subsections will separately show the details.

3.1Basic idea

The framework which is illustrated in Fig. 4 is reviewed in this subsection. Two main phases are consisted of in our framework,\({\text{i.e.}},\), training phase which is shown in the solid box of Fig. 4, and validation phase shown in the dotted box of Fig. 4. The kernel of framework is setup and application of K-Nearest Neighbor (KNN) prediction model. The prediction model is constructed by extracting features from training programs during the training phase as the input of the KNN learning algorithm. The partition schemes for unknown programs are predicted by the already trained prediction model during the validation phase. The programs used in validation phase are different from the training programs. During the period of collecting hybrid sample set [32], a cross validation method [46] is used to build the training sample set.

3.2Construction of predictor

The extraction of static and dynamic features is taken charge of by a profiling pass, when the benchmarks are inputted to Prophet [26,28]. With these features, the corresponding feature vectors provided to the machine learning tool are generated by Prophet. KNN is used to generate a predictor which can predict an appropriate partition scheme for an unseen program.

The reason why KNN is selected as the primary learning algorithm can be summarized as:

1. 1.

   As one simple and effective algorithm, KNN is used to perform clustering [47];

2. 2.

   In accordance with similarity distances among samples, KNN can implement classification.

Once training programs are used to train KNN prediction model, this model can predict the partition for unknown programs. In IDaTPA, five key points are included, including constructing sample set, extracting features, calculating similarity, prediction model, applying prediction model.

The next key step is to make a comparison of the similarity, between the features from training programs and the generated features (in the form of vectors) from unseen programs. It is a searching process to compare these two type of features within the training samples’ space. When an unknown program comes, IDaTPA calculates the similarity between it and every training program to achieve the most similar training sample. The Sect. 5 shows the specific similarity comparison.

Overall designing framework

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In order to realize the prediction of the ideal partitioned structures for irregular programs, thread partitioning can be regarded as a problem of machine learning. To predict the ideal partitioned structure\(H_{par}\) when given the essential feature vector\(X_{orig}\), we wish to have a model built. However, an encountered particular problem in training model is that the number of irregular programs meeting the need of training model is few. In order to handle this problem, we study these features affecting program’s speedup and use them to characterize an irregular program. Then, we complete the building of feature sets from Olden benchmarks. These features can permit us to train our model on larger training samples.

4.1Construction of sample set

It is very important to construct an appropriate sample set for IDaTPA, as it takes charge of providing knowledge. Conventionally, there exists a bottleneck for conventional sample generation approach, which can not generate adequate adaptive samples. To break this bottleneck, a hybrid sample generation approach was proposed in the article [32,48]. With this approach, we first of all generate samples expressed with mips codes by heuristic rules, which consist of SP and CQIP; secondly we manually complete the adjustment for the positions of SP and CQIP as well as building precomputation-slice (p-slice) for performance improvement of every sample; then building a model to guarantee that the probability of adjusting to the satisfied partition positions is increasing. When implementing this approach, we take three measures: preserving the satisfied solutions, summarizing greedy rules, bias weighting. In this way, IDaTPA increases the adjustment frequencies for subroutines with high called time as well as realizes the preservation of the satisfied partition positions, so to improve speedups stably.

4.2Process of sample generation

To a procedure, it can own various partitions. IDaTPA firstly selects the best partitioning scheme which can achieve the highest speedup. When one sequential program turns up, the program is firstly transformed to one SUIF intermediate representation (SUIF IR). The profiler analysis module in Prophet [26] is used to pass though the IR programs. The execution statistics, including the branch probability, the number of dynamic instructions for subroutines and loops are collected. Then, IDaTPA partitions annotated SUIF IRs into multithread programs using the back-end HR-based thread partitioner. The SUIF IRs are regarded to be input by the MachSUIF and Linker, as well as are used to generate threaded mips programs. Then, we manually make an adjustment of the partition flags (SP-CQIP) in mips codes, so to generate the training samples. On the Prophet simulator, by using generated partitioning structures for every procedure, we generate a candidate training sample by selecting the satisfied partitioning structure. Furthermore, we store the satisfied partitioning structure so to complete the generation of training samples.

4.3Expression of knowledge

4.3.1Extracting features

Conventionally, compiler researchers used representations of the programs’ features or intermediate representations (IRs) [49] with fixed-length. Extracted from programs, they are gathered during compilation time. Afterwards, we apply vector-based features to express the weighted control flow graph (WCFG) for IDaTPA. The profiling pass generates the feature graphs, from which static and dynamic features are extracted. Olden and SPEC2020 benchmarks [50,51] are two primary input programs. The main influence factors on program speedups consist of data dependence, control dependence, thread granularity and load balance. Hence, programs’ features mainly include dynamic instruction number, DDD, DDC, loop branch probability, as well as critical path. The static features are shown in the top left dotted box of Fig. 2, while the dynamic features are presented in the bottom-left dotted box of Fig. 2. Along the critical path, the numbers of basic blocks are denoted with\(P_{i}\) in arrayP, and basic blocks are represented with nodesA,B,..,H. The loop branch probability between the\(i_{th}\) and the\(j_{th}\) basic block is represented with\(M_{ij}\) in matrix\(M_{1}\). Respectively denoted with\(M_{ij}\) in matrix\(M_{2}\) and\(M_{3}\), the data dependence count (DDC) and data dependence distance (DDD) are all between the\(i_{th}\) and the\(j_{th}\) basic block. Table 2 presents the specific features and descriptions.

4.3.2Partitioning scheme

Training set and validation set are from the same input set and divided. First, after an intermediate pass, we generate the control flow graph (CFG) by using partition compiler (in the Prophet). Then, generated by profiling model, profiled feature information is annotated to the CFG using a structural analysis method, so to generate WCFG. The relative branch probabilities are denoted on all edges. Then, IDaTPA makes use of a structural analysis to traverse programs’ CFGs to WCFGs, loop regions are also identified. Then, loop regions are induced into one super node, that has an entry and an exit node, and WCFG turns to super control flow graph (SCFG), in which loop region is traversed to an abstract node. Every node of the SCFG represents either basic block or super basic block, representing loop region. Figure 3a shows a CFG. After loop region induction and structural analysis, one super basic blockI (shown in the Fig. 3c) attributes four basic blocks {C,D,F,E} in the dashed box of Fig. 3b. The gray node denotes one super basic block in the Fig. 3c while the white node represents one basic block (in the Fig. 3a, b). The weight on every edge of Fig. 3b, c denotes branch probability. The Fig. 3a denotes a control flow graph (containing a loop structure, namely C → D → F → E → C → ... → C → ...). The Fig. 3b represents a WCFG, in which one loop structure is represented with the dotted box. From the Fig. 3b, c, a super block (also calledI which induces the loop structure: C → D → F → E → C → ... → C → ...)

Transformation from CFG (a) to WCFG (b), and finally to SCFG (c)

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Thread partition decisions generated by different partition thresholds

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Extracted from thread partition rules [15,25], and thread partition algorithms, a flow chart of thread partition decision is shown in Fig. 4. From this figure, we can infer that modifyingULoSD, DDC, LLoSD, ULoTG,LLoTG can lead to different partition decisions (continue to execute the original thread? or spawning new thread? or others). For these reasons, a vector\(H =[H_{1}, H_{2}, H_{3}, H_{4}, H_{5}]\) represents the desired thread partition scheme and\(h_{i}=[h_{i1}, h_{i2}, h_{i3}, h_{i4}, h_{i5}]\) (\(i \in N\)) represents the sample partition scheme, all of which have five thresholds: the lower limit of thread granularity (LLoTG), the upper limit of thread granularity (ULoTG), data dependence count (DDC), the lower limit of spawning distance (LLoSD), and the upper limit of spawning distance (ULoSD). As the effectiveness of thread partition is determined by these five parameters, and [LLoTG,ULoTG,DDC,LLoSD,ULoSD] represents the partition scheme. For example, a partition scheme could be [10, 50, 18, 20, 30]. These values indicate that thread granularity ranges from 10 to 50, and data dependence count is no more than 18, and spawning distance is set from 20 to 30 during the period of thread partition, and\(H_{1}\),\(H_{2}\),\(H_{3}\),\(H_{4}\),\(H_{5}\) can be expressed as follows:

\(H_{1}=LLoTG = 10\),\(H_{2}=ULoTG = 50\);

\(H_{3}=DDC = 18\);

\(H_{4}=LLoSD = 20\),\(H_{5}=ULoSD = 30\).

Based on generated samples [32,48], IDaTPA obtains every sample’s partition scheme\(h_{i}\)(i\(\in\)N) for every sample with the method of mathematical statistics, and Fig. 4 shows the specific flowchart. Although every sample is denoted by a graph,T={X,H} represents the node in the graph, where program features are represented by\(X\), and the satisfied partition scheme is denoted byH, consisting of five partition thresholds, namelyLLoTG,ULoTG,DDC,LLoSD,ULoSD (Table3).

According to weighted summation of the partition schemes\(h_{1}, h_{2},\ldots , h_{k}\)(\(k \ge 1\) &\(k \in N\)) for the nearestk sample programs [32], the partition schemeH is determined by a new program. And, the five thresholds {\(H_{1}\),\(H_{2}\),\(H_{3}\),\(H_{4}\),\(H_{5}\)} are separately calculated. Before calculating,\(h_{ij}\) (\(1\le i \le k\) &&\(i \in \hbox {N}\)), as the\(j_{th}\) threshold of the\(i_{th}\) neighbour sample program is first determined. Then, the weights of partition schemes are designed by using coefficients of Taylor’s formula [52]. The Taylor’s formula is shown in the formula (2).

When we replace\(g(x)\) with\(e^{x}\), then the formula (3) comes up.

In formula (3), whenx = 1, and both sides of the formula are uniformly divided by\(e\), then the formula (4) turns up.

Using\(\frac{2}{e},\frac{1}{e*2!}, \frac{1}{e*3!},\ldots ,\frac{1}{e*k!}\), the formula (5) shows the weights of partition schemes (\(h_{1}, h_{2},\ldots , h_{k}\)) for the\(k\) nearest neighbours.

We conclude the reasons why to use formula (4) to choose the coefficients can be concluded as follows:

• As the coefficients,\(\left\{\frac{2}{e}, \frac{1}{e*2!}, \frac{1}{e*3!}, \ldots , \frac{1}{e*k!}\right \}\) have data range from 0 to 1;

• In order to make the closer neighbours take a bigger effect to determine the final partition scheme, the coefficients are gradually descending;

• The summation of all coefficients is no more than 1.

  $$\begin{aligned} \left\{ \begin{aligned} h_{i}&=[h_{i1},h_{i2},h_{i3},h_{i4},h_{i5}],i\epsilon [1,k] \& k\epsilon N;\\ {H}_{j}&=\frac{2}{e}{h}_{1j}+\sum _{i=2}^{k}\frac{1}{i!e}{h}_{ij}, j =1,2,\ldots ,5. \end{aligned} \right. \end{aligned}$$

  (5)

  where, the\(j_{th}\) desired threshold is denoted by\(H_{j}\), and\(h_{ij}\) indicates the\(j_{th}\) threshold for the program’s\(i_{th}\) nearest samples, andk is the number of the selected similar samples. From the sample set (also called\(^{''}\)Hybrid Sample Set\(^{''}\) in Fig. 2), partition knowledge consisting of program features and its satisfied partition scheme is inherently included, IDaTPA acquires the partition scheme (\(h_{j}\)) of unknown program from its neighbors.

Formula (5) generates the good partition thresholds and threshold combinations. Using this scheme, IDaTPA partitions the input programs for speculative execution.

5.1Similarity calculation

In order to handle this problem, a KNN model is proposed. As can be seen from Fig. 2, features of the input program\(x_{q}\) and its neighbour\({x}_{i}\)(\(i \le k\) &&\(i \epsilon \hbox {N}\)) are firstly extracted, which are shown in the formula (6).

The ranges of extracted feature variables\({N}_{1}\sim {N}_{8}\) vary differently, for example\({N}_{4}\),\({N}_{5}\) vary from 0 to 1 while\({N}_{6}\),\({N}_{7}\),\({N}_{8}\) are all more than 1, shown in the expression (7).

In order to handle the problem that the dimensions of variables in the feature vector are not uniform and the ranges of values are inconsistent, this paper normalizes the variables within feature vector with the formula (8).

where,\(N_{i}^{*}\) is the value of normalized\(N_{i}\),max(\(N_{i}\)) andmin(\(N_{i}\)) are respectively the maximum and minimum of\(N_{i}\). As there may be many branches and loops in a sample, so\(N_{4}\) and\(N_{5}\) can be expressed with vectors, namely\(N_{4}=<N_{4}^{1},N_{4}^{2},\ldots ,N_{4}^{m}>(m \epsilon \hbox {N})\),\(N_{5}=<N_{5}^{1}\),\(N_{5}^{2},\ldots ,N_{5}^{n}>\)(\(n \epsilon \hbox {N}\)), wherem,n are respectively the dimensions of\(N_{4}\) and\(N_{5}\), and\(N_{4}^{i}\) (\(i \epsilon \hbox {N}\)) is the branch probability of the\(i_{th}\) branch,\(N_{5}^{j}\) (\(j \epsilon \hbox {N}\)) is the branch probability of the\(j_{th}\) loop branch.\(N_{4}\) and\(N_{5}\) are not normalized, while\(N_{1}\),\(N_{2}\),\(N_{3}\),\(N_{6}\),\(N_{7}\),\(N_{8}\) need be normalized. The similarity distancesim(\(x_{q}\),\(x_{i}\)) is calculated with formula (9).

where,\(N_{i}\) and\(N_{i}^{'}(i =1,2,3,4,5,6,7,8)\) are respectively variables of\({x}_{q}\) and\({x}_{i}\), and\(({N}_{i}^{'})^{*}\) is the normalized result of\(({N}_{i}^{'})\).

5.2Thread partitioning based on KNN

5.2.1Generation of partition scheme

Once an unseen program comes, its partitioning scheme is generated in accordance with the schemes of its nearest training samples. The assumption exists that machine learning approach is used to partition the program into parallel threads. A ML-based prediction model is built after generating sufficient training samples. IDaTPA is applied to predicting the unseen programs’ right partition scheme, rather than the sequences of SP and CQIP directly. IDaTPA makes use of a classifier model, namely K-nearest neighbor (KNN). For every training sample, its feature vector\(X_{orig}\) and the best partitioning scheme\(H_{par}\) (obtained in the Table3)  are recorded. That means the listtraining_samples stores all training samples\(<x_{org-i}\),\(h_{par}(x_{org-i})>(i \epsilon \hbox {N})\) to build KNN prediction model.

As described above, in order to predict the unseen program’s partition scheme, IDaTPA builds the prediction model as shown in Fig. 2. Once every program’s best partition scheme has been predicted, and a new irregular program comes, the scheme is then applied to determining the sequence of SP and CQIP, which can generate a partitioned program.

As one effective and simple classifier, IDaTPA has the training samples stored simply, and generates samples which are postponed until classifying one unseen sample. When one new sample comes, its k nearest neighbor training samples stored in the training sample set take vote to classify the new sample. One key difference between the conventional KNN approach and the approach discussed in the paper is that the classification label of the former is a target function value while the classification label of the latter has three target function values that represent a partitioning scheme.

By comparing the input program’s feature vector to the known program’s feature vectors with Euclidean distances (calculated in the formula (9)), IDaTPA locates thek nearest training samples\(x_{i}\) (\(1\le i \le k\)). Once we find thek nearest training samples, the predicted classification label is obtained by adding weight values to the\(k\) nearest training samples, so to guarantee model approximation and generalization. We do this because if the input program\(x_{q}\) and the\(i_{th}\) (\(i \le k\) &&\(i \epsilon \hbox {N}\)) nearest training samples\(x_{i}\) are more similar, they should have a more similar partitioned scheme, so more similar they are, the bigger the weights, but the sum of weights should be equal to 1.

In order to assign the weights, we introduce the Maclaurin series [53] for the exponential function\(e^{x}\) with the formula (3) in this paper. Given thatx =1 and divided bye on both sides of the Eq. (3), then we have the Eq. (4).

Within this model that the closer the distance between\(x_{q}\) and\(x_{i}\) is, the bigger the weights are and the sum of weight is equal to 1, we can obtain the assignment weights in the formula (10).

wheren is the sequence number based on the distance of the\(i_{th}\) nearest training samples\(x_{i}\) from the new sample\(x_{q}\).

The final partition scheme is gotten in accordance with the\(3_{rd}\) part of Sect. 4. Because our target function is five-dimensional feature vector\(H_{par}\), predicted results of the new sample\(x_{q}\) are equal to the product between target function values of the K nearest training samples and their weights, respectively. Only if the partitioning scheme has been predicted successfully, we use the partitioner (in the\(3_{rd}\) part of Sect. 4) to make the input program partitioned into parallel threads by applying the scheme.

5.2.2Application of partition scheme

As shown in the algorithm of K-nearest Neighbor Classifier (in Table 4), applying the importance degree based thread partition tool to unknown applications is one of the most important part in IDaTPA. Once training set are decided, we make a comparison between an unseen procedure and every sample selected from training set, so to generate its partition scheme\(x_{q}\) in accordance with the partition schemes of itsk nearest neighbours. The parameterk indeed affects performance, and a cross-validation method [54] is used to get the satisfied value ofk. With this method, the value ofk ranges from 1 to\(\sqrt{S\_sample }\) (whereS_sample is the number of samples). Then, the partition estimation of functional relation betweenk and cross-validated accuracies is completed, and IDaTPA obtains the optimalk once the accuracy reaches peak.

From Fig. 2, it can be seen that for the partitioning of the new program, the first step is to extract and express its features; Secondly, construct a prediction model based on the KNN learning algorithm; Then, based on the thread partitioning framework and pre-processing results, build a partitioning tool and generate the partitioned program. Note: The above process is completed during the program compilation phase.

The partition process can be divided into 3 subprocedures: construction of p-slice [15], nonloop partition, loop partition. The following sections respectively describe them.

(1) Loop partition

When one function is partitioned, IDaTPA firstly identifies and partitions loops. The loop partitions’ decision relies on profiling information about size of loop, number of iteration number, and DDC between successive iterations of loop. Once it is profitable to spawn a thread, one new thread is spawned. When a loop owns appropriate granularity and DDC between inter-iterations is small, its iteration is regarded as a candidate thread. As the performance improvement of small loops can not offset the overheads of spawning new threads, they need not be parallelized. Otherwise, IDaTPA unrolls loops so to leverage the parallelism of them. Table 5 shows the detailed loop partition algorithm. For loop region, the\(2_{nd}\) part of Sect. 2 is the heuristic rules which show the locations of SP and CQIP. In accordance with DDC existed between successive iterations, SP points which spawn candidate threads are inserted at some points within loops, and the CQIP points of candidate thread are placed before the loop branch instructions. Figure 7 shows the SCFG which formalizes programs, and the Algorithm 2 presents the overall partition after the conversion from CFG to SCFG, then Algorithm 1 presents the partition of loops after overall partition. The next part presents an example of thread partition for a loop,namely S = 1 + 2 + 3 + ... + 100.The codes for this example is shown in the Fig. 5.

The code for the example

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Assume the values of LLOTG,ULoTG,LLoSD,ULoSD,and DDC are 10,20,15,21,10, then the code with SP and CQIP points are shown in the Fig. 6.

The code for the example

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An example of non-loop partition, (a) is an original control flow graph, (b) is the super control flow graph in which the node A is a super node, (c) indicates the likely path from the start_block to end_block

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(2) Nonloop partition

The pseudo code of nonloop partition is shown in the Table 6. By calling the functionpartition_thread recursively, IDaTPA partitions the code segment between two basic blocks into multiple threads. Along the most likely path in Algorithm 2, the basic blocks fromstarting_block toending_block form thecurr_thread. Oncecurr_thread equals NULL, the end of previous thread is the beginging of current thread. The reason why the candidate thread iscurr_thread is ascurr_thread is small or there is too much data dependence. The postdominator ofstart_block ispdom_block, which is the control-independent point for current basic block. Thelikely_path is the most likely path fromstarting_block topdom_block. If\(H_{3}\) is the maximal dependence from current thread to future thread, good data dependence counts are obtained by the functionfind_good_dependence. To make thread granularity balance, the ULoTG and LLoTG are set so to yield the best speedups at runtime. A new thread (shown in Fig. 7a) is generated by partitioning thecurr_thread, when thecurr_thread satisfies two conditions: DDC is the maximum of its dependence with its successor thread, and its granularity is within its limits. If thecurr_thread’s granularity is too big, candidate threads will be generated by means of implementing partition for the code betweenstart_block and control-independent basic block, shown in Fig 7b. Otherwise, if the granularity ofcurr_thread is too small, IDaTPA will not create a new candidate thread, andfuture_thread is appended tocurr_thread simply (shown in Fig 7c).

(4) Loop partition’s example

Figure 8 presents one example of loop partition. During this process, IDaTPA firstly constructs CFG for every function and identifies the loops with the Prophet compiler through analyzing data and control flows. Then, Algorithm 1 presents the loop partitioning and generation of multiple threads, in which a common scenario is that a new thread starts at the beginning of the next loop, if the loop size meets granularity requirements and the dependency between adjacent loops meets threshold requirements. Moreover, the CFG is turned into SCFG which reduces loops to nodes, then Algorithm 1 presents one scene that SCFGs are partitioned so to generate speculative threads. Compared to non loop partition, loop partition is relatively simple. Only DDC of successive iterations is not more than certain threshold and the thread for loop has proper size, one SP is inserted at the loop iterations’ beginning node, while one CQIP is inserted before the branch instruction at the ending node of loop. Hence, any iteration can spawn its next iteration to generate a candidate thread, and execute iteration bodies in parallel.

An example of loop partition

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(5) Nonloop partition’s example

Figure 7 presents one nonloop partition’s example. Once the IDaTPA is trained, and has completed the similarity comparison, the good partition schemeH comes, then the specific values of [\(H_{1}\),\(H_{2}\),\(H_{3}\),\(H_{4}\),\(H_{5}\)] are obtained.

Once encountering a branch, the example in Fig. 7 considers the most likely path to be the path B0-B2-B3-BA-B8-B9 through selecting the branches with bigger probabilities. T1 is initialized with B0, and the SCFGs are partitioned into multiple threads with the following steps.

Firstly, IDaTPA respectively finds the nearest postdominator node B3 for node B0 and the most likely path from B0 to B3. On the most likely path node B2 is found and classified into thread T1, then within or beyond [\(H_{1}\),\(H_{2}\)] the size of thread T1 is judged. If so, we make an assumption that the DDC between sub-graph {B0, B2} and successive sub-graph {B3, BA, B8} is no more than DDC, the thread T2 is initialized with node B3. Then, B9 is considered as the nearest post-dominator node of B8, and BA-B8 is regarded as the most likely path. Assume that a good DDC from subgraph {B3, BA, B8} to subgraph {B9} is not more than the threshold of DDC, so the successive thread contains the nodes {B3, BA, B8}, exceeding the upper limit of thread size, hence a new thread T3 owns a boundary (node B9). Then IDaTPA partitions subgraph {B3, BA, B8} so to generate smaller threads. The partition process ends once these constraints are satisfied. Once IDaTPA determines the thread boundaries, it will further build every candidate thread’s p-slice in order to diminish data dependence between the producer thread and its successive thread.

5.3Analysis of time complexity

The phases:training and prediction are two primarty processes to spend time for the IDaTPA. During the training phase, the approach used to learn the knowledge of thread partition is KNN, which is also applied to predicting thread partition during prediction phase.

The prediction of partition schemes for unknown programs is implemented after the nearest\(k\) samples are selected. Two processes form the prediction process, namely searching the partition positions and inserting partition instructions (including spawning point (sp) and control quasi independent point (cqip)).

Of the two processes, as the time spent in inserting partition instructions is little, so can be ignored. Assume the number of partition instructions is\(k\), then the time spent in inserting sp and cqip is:

\(n \times (n -1)+(n -2)\times (n -3)+(n -4)\times (n -5)+\cdot \cdot \cdot \cdot +(n -k )\times (n -k -1)=\Theta (n ^2)\), so the time complexity is\(\Theta (n^2)\).

This section introduces the experimental setup, and provides the description of Prophet simulator and benchmarks during the evaluation. Finally we present the results’ analysis and discussions.

6.1Experiment’s configuration

We implement IDaTPA on Prophet (Fig. 9 shows Prophet’s Module Chart), which is built on SUIF/MACHSUIF [55]. At the intermediate representation(IR) of SUIF, we complete the compiler’s analysis. SUIF-IR produces the profiling information in the form of annotation by profiler of Prophet. The Prophet profiler interprets and executes information, including dynamic instruction number, prediction of control flow path, and prediction of data values. The Prophet’s simulator can simulate\(1\sim 64\) pipelined mips-based R3000 processing elements (PE) and IDaTPA is performed on 4 PEs or 8 PEs. Every PE issues 4 instructions orderly per cycle, and fetches instructions and executes them from one thread. Every PE has a private multiversioned L1 cache with latency of 2 cycles. Multiversioned L1 caches are used to perform cache communication and buffer speculative results of PEs. With a snoopy bus, 8 PEs share one write-back L2 cache. Table 7 shows the simulator’s parameter configuration.

The benchmarks that IDaTPA evaluates include Olden [56] and SPEC2020 [51]. As one popular benchmark to study irregular programs, SPEC2020 and Olden benchmarks deal with complex control flows, irregular data and pointer-intensive structures. The benchmarks comprise of dynamic structures, e.g., lists, DAGs, and trees et al, which are all hard to be parallelized with conventional approaches.

IDaTPA validates its results with the method of leave-one-out cross-validation. In the method, the program to be partitioned is first of all moved from training set, and based on the left programs a prediction model is built. There is an advantage that the built prediction model never sees the programs to be partitioned before. By applying the prediction model, the left programs’ partition scheme is built. Every program is performed similarly in turn. To solve memory dependence, the paper uses multi-version caches, and a register file to solve register data dependence.

Module chart of prophet

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6.2Results and analysis of experiment

This section focuses on the benefits of IDaTPA firstly and reports its performance on a platform with 4-core. Furthermore, we analyze what type of heuristic is important to every program. Next, we evaluate the approximation of KNN model. Finally, in order to verify the portability of IDaTPA, we reimplement our approach on 8-core platform and deliver a stable performance improvement. In experimental section, HR-based thread partitioning approach [33] and IDaTPA are respectively implemented on Olden and SPEC2020 benchmarks.

6.2.1Speedup comparison

The experimental results presented in Fig. 10 present overall performance enhancements while using Olden benchmarks with HR-based approach and ANN-based approach [57], and IDaTPA respectively. Seen from Fig. 10, all programs obtain higher speedup improvements in different percentages with IDaTPA, especiallyhealth andperimeter have obtained high performance improvement. For further analysis, we make a definition of increasing rate (rate_inc) as:

Seen from the rightmost column of Table 8’, therate_inc changes from 1.01% to 15.89%. Compared to the HR-based approach, these experimental results show that IDaTPA can exploit more parallelism of speculation and yield a better performance enhancement for all Olden benchmarks.

Additionally, Tables 8 and9 respectively present the dynamic information as well as thread statistics generated by Prophet. The breakdown of speculative execution with HR-based approach (ending with 1) and IDaTPA (ending with 2) are also shown in Fig. 15. The speculative success rate is referred to be SR. Data dependence violations in verification is referred to be DDV. CV indicates violation of control dependence in thread squash. LP indicates a situation of spawning a thread with low priority during threads’ withdrawing.

Programbh simulates the motion of particles in space using an O(nlogn) algorithm to complete the computation of particles’ accelerations. This refers to be a classic n-body problem. From Table 10, we can see that parallelism of programbh is not only from loops but also from non-loops. In loops, the number of loop body size and iterations are 200.45 and 9.14 (Table 11) on average, respectively. At the same time, programbh adopts the main data structure of heterogenous octree, complex data dependence exists in both loops and non-loops. Compared to the HR-based approach, IDaTPA adopts thread size and smaller data dependence counts according to features of programbh (Table 11), resulting that the number of the spawned threads decreases. However, as to the speculative success rate, programbh has some increase, in the end, the number of successful spawned threads increases (Table 8). At the same time, the original HR-based used nearly identical partitioning scheme with IDaTPA, so we only provide a 1.78% performance improvement for programbh.

Speedups for olden benchmarks between HR-based approach and IDaTPA

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Programem3d simulates the electro-magnetic waves’ propagation in a 3D object. From Table 10, we can clearly see that programem3d holds 72% dynamic instructions of loop structures, so its parallelism is mainly derived from the loop structures. The average iteration time is 8.73 which is of medium scale (Table 11) and many larger loops averagely contain 265.99 dynamic instructions, so programem3d owns larger loop-level parallelism and can achieve the highest speedup for both the HR-based approach and IDaTPA (shown in Fig. 10). In accordance with the characteristics of programem3d, IDaTPA makes use of thread size and less data dependence counts to partition the sequential programs into multithreadings than the HR-based approach (Table 9). Although the success ratio is still unchanged compared with HR-based approach, more speculative threads are exploited by IDaTPA to perform parallel computation and the speedup achieves a 2.66% performance enhancement.

Programhealth simulates the Colombia health-care system with a four-way tree. As shown in Table 10, programhealth spends a mount of time in executing loop structures. Just to the programem3d, the major source of programhealth is loop-level parallelism. Previous HR-based thread partitioning approach uses the same data dependence counts to partition loops into multithreads while IDaTPA adopts different data dependence threshold predicted by machine learning for exploiting loop-level parallelism, resulting that thread size and dependence between threads are more smaller than previous approach (Table 9). Compared to the HR-based approach, the spawned success ratio decreases, but the spawned threads have a significant increase, so the successful spawned threads also increase (Table 8). Hence, IDaTPA allows more speculative threads to perform speculative execution and improve a 15.34% performance.

Programperimeter computes the perimeter of a quad-tree encoded raster image. Programperimeter has no any loop structure (Table 11) and all the parallelisms come from non-loop regions (Table 10). Parallelism of programperimeter benefits from speculative execution of functions and multithreadings decomposed by large function body, since the returned value of function is hard to be predicted, so the spawned success ratios are all lower. Compared to the HR-based approach, IDaTPA selects appropriate partitioned parameters for all functions by machine learning and selection of partitioned parameters is not affected by loop structures. Using machine learning, programperimeter achieves the highest performance improvement of 15.89%.

Programvoronoi randomly generates a set of points and completes computation of avoronoi diagram for these points. As shown in Table 11, we know that the number of dynamic instructions is split equally between non-loops and loops, so the parallelism should be exploited from both of them. Although loop size is 137 that is of medium scale, the iteration time which is only 1.3 is very small, loops contains fewer parallelism (Table 10). Almost all parallelism comes from parallelism of larger functions, especially forput_voronoi_diagram anddo_merge. Compared with the HR-based approach, IDaTPA finds the best partitioned schemes by prediction model for each function. Nevertheless, the partitioned parameters predicted by IDaTPA are not much different from the heuristic parameters, so the speedup just has a 1.01% performance enhancement.

Programtreeadd walks a tree recursively to sum each subtree and is a very simple program. Just like programperimeter, programtreeadd does not have loop structure (Table 11), loop partition algorithm is not effective on it, so the parallelism of programtreeadd comes from non-loop region (Table 10). Table 8 indicates that programtreeadd has a certain increasement of the speculative success rate (Fig. 15) and IDaTPA has an increasement for the number of spawned threads, resulting that more successful threads are to be executed in parallel for exploiting large parallelism. Programtreeadd has simple data dependences and recursive function calls. These function calls are speculatively executed in parallel on multicore. Compared with previous HR-based approach, IDaTPA adopts smaller data dependence counts and thread size for programtreeadd. At the same time, programtreeadd has a lower squash rate in DDV and CV by using IDaTPA (Fig. 15). According to characteristics of programtreeadd, this program is more suitable for fine-grained parallelism and obtains a 6.88% performance improvement (Table 8).

Programpower handles the problem of Power-System-Optimization. The problem can be described as follow: given a power network represented by a multi-way tree with the power plant at the root and the customers at the leaves, programpower uses local information to optimize the prices that are benefit to the community. Table 11 shows programpower owns 20 loop structures which are of the bigger body size. At the same time, the loop structures hold 59.5% dynamic instructions by program’s profiling (Table 10). Compared with the HR-based approach, IDaTPA picks up a larger data dependence count to partition programpower into multithreads (Table 9). Through analysis, the parallelism primarily comes from loop-level parallelism, especially inmain andoptimize_node. From Table 8, we can clearly see that although the spawned success ratio has not been changed, the number of spawned threads has increased, so the success spawned threads have increased. Finally, IDaTPA obtains a 1.19% performance improvement.

Programtsp estimates the planar graph’s shortest Hamiltonian circuit. Table 11 indicates that although 7 loop structures exist in programtsp, in fact only 2 loops are executed. But, the average dynamic instructions of loop bodies are 1267.25 and the average times of loop are 94.5. So the percentage of dynamic instructions in loops is 40.8% in programtsp. At the same time, in non-loop regions, there are several larger function bodies, namelyconquer,merger andmain functions that are partitioned into multithreads to be executed in parallel. Previous HR-based approach uses data dependence count 5 and thread size (15–60) to partition programtsp for speculative execution while IDaTPA adopts different DDC and thread size to partition different functions. Compared with previous theories and methods, although IDaTPA generates fewer spawned threads, the successful spawned threads and success ratio increase. Finally, IDaTPA yields a 2.89% performance improvement.

Programmst uses Bentley’s algorithm to compute the minimum spanning tree of a graph. The structures ofmst andpower are similar, butmst has a much bigger dynamic instructions for non-loops (shown in Table 10). On the one hand, 8 of the 13 loops which have larger thread sizes perform execution speculatively. On the other hand, larger functionMakeGraph,BlueRule andAddEdges are partitioned further into multithreads which are executed on multi-core by thread partitioning approach. Compared to the HR-based approach, IDaTPA performs the predictions of the best partitioned scheme for every function by machine learning, so the success ratio of spawned threads and the number of spawned threads increase. Using machine learning for partitioning, programmst obtains a 3.42% performance improvement.

Programbisort allocates as well as sorts a random set of integers: one forward and one backward. From Table 11, we can clearly see that 3 loop structures that have fewer dynamic instructions exist and as a matter of fact only 2 loops are executed. Meanwhile, the percentage of dynamic instructions in loops is only 26.3%. So the existing parallelism is derived from functions which are in parallel executed. Because programbisort which has complex data dependence, makes use of binary tree in order to store the numbers, both the HR-based approach and IDaTPA, respectively achieve speedups: 1.27, 1.35. Compared to the HR-based approach, IDaTPA chooses these program-aware partitioned parameters that have larger thread size and data dependence counts to exploit all possible programs’ parallelism. Finally, IDaTPA achieves a 6.31% significant performance improvement.

Table 9 indicates that the mean as well as variance of thread size produced by IDaTPA are smaller than HR-based method. It means that improved load balance and moderate thread size can yield a significant performance improvement. At the same time, we can also see that some programs (em3d,health,voronoi andpower) exploit coarse-grained parallelism whose thread granularity is more than 35 dynamic instructions while certain programs (bh,perimeter,treeadd,tsp,mst andbisort) exploit fine-grained thread parallelism.

Based on the above analysis, three conclusions are presented: 1. IDaTPA can exploit fine-grained and coarse-grained parallelism of program. What type of parallelism is benefit to the speedup improvement, is decided by the programs’ characteristics; 2. Although sacrificing certain success rate of speculative execution can enhance the time overheads of thread squash, compared to the HR-based approach, IDaTPA can yield speedup improvement by extracting more candidate threads to be executed in parallel; 3. The speedup of speculative multithreaded program is not only associated with the number of spawned threads and success rate but also with thread size and load imbalance. Whether or not program’s speedup increases depends on that the overheads caused by speculative parallelism are less than benefits by speculative execution. In conclusion, IDaTPA yields a 5.75% performance improvement for Olden benchmarks than the HR-based approach and yields an average speedup of 1.80. These results indicate that IDaTPA is more stable to find every program’s best partitioning scheme.

6.2.2Comparison with best-found performance

Machine learning can learn prior knowledge from known facts and perform well on unseen instances. Since a program has a large number of partition structures, it is very time consuming to find the known facts in SpMT. In our work, we generate 414,687 different partition structures for every program and select the best partition structure as a training sample that is called “Best-Found” performance.

Compared with HR-based approach, IDaTPA performs well and we wish to know whether or not there is further improvement room by assessing how close the Best-Found is to IDaTPA’s performance.

Performance comparison with best-found

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In Fig. 11, we compare IDaTPA with the Best-Found. Forpower,mst andtreeadd, they almost reach this Best-Found performance. But for other programs such asbh,em3d,perimeter,bisort,voronoi,tsp, andhealth, there is significant improvement room. Although IDaTPA outperforms prior HR-based approach on these programs, it could have done better. Overall we achieve 90.64% of that maximum performance and the results show the programs’ satisfied partitioning strategies vary in accordance with their characteristics.

These examples indicate that programs with different characteristics apply different partitioning strategies. Essentially, the HR-based approach is a strategy of one-size-fits-all. By using a machine learning technique, IDaTPA handles this problem. It uses prior knowledge to choose and apply the program-specific partitioning strategy according to target programs’ characteristics, yielding a better performance than hardwired heuristics.

6.2.3Speedups of SPEC2020 benchmarks with IDaTPA

Figure 12 indicates the general speedups with IDaTPA for part of SPEC2020 benchmarks. X-axis denotes selected benchmarks, including164.gzip,176.gcc,177.mesa,181.mcf,188.ammp,183.equake,186.crafty,197.parser,253.perlbmk,254.gap,255.vertex,256.bzip2,300.twolf, while speedups after using IDaTPA are shown along Y-axis. The benchmarks:254.gap,186.crafty and197.parser deliver significant performance improvement while177.mesa,300.twolf,183.equake,188.ammp,255.vertex,256.bzip2 have little performance improvement.

Figure 13 shows the speedups of IDaTPA for SPEC2020 benchmarks in Prophet. In Fig. 13, X-axis indicates Olden and SPEC2020 benchmarks, while Y-axis shows the speedups over one PE (processing element). With the support of Prophet [26], IDaTPA is performed upon it, and gets speedups over one PE.

General change for part of SPEC2020 Benchmarks

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Speedups of SPEC2020 benchmarks with HR-based approach in prophet

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6.2.4Portability of IDaTPA

In this section, we verify the portability of IDaTPA on the Prophet simulator with 8-core (a new platform). On the 8-core platform, training samples are re-collected and used to construct the machine learning model. Note that the same programs are applied to training IDaTPA which is used on the 8-core platform.

Figure 14 shows the speedups of HR-based approach and IDaTPA on the 8-core platform. Compared with the HR-based approach, IDaTPA shows a more stable and better performance improvement. Figure 14 indicates that all programs yield higher speedup improvement with different percentages and the increasing rates of performance improvement vary from 1.12% to 15.89%, especially programhealth,perimeter,bisort,mst,treeadd have improved performance significantly. Two important factors lead to this significant performance gain: firstly, IDaTPA provides the satisfied thread partitioning scheme for every program according to their features; secondly, because these irregular programs have more inherent parallelism, the parallelism can be adequately exploited in the case of enough core resources. Programpower exhibits a meager increase. Although IDaTPA can predict the best partitioned structures for these programs, which have complex control and data dependence. So even if processor resources are sufficient, these speculative threads are withdrawn frequently and cannot make effective use of core resources. Consequently, the speedups of these programs with complex dependence do not change when core number increases to a certain number.

Performance comparison on 8 core platform

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Breakdown of speculative execution

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Although IDaTPA delivers a better performance improvement on 8-core, however, just like in the case 4-core, there are some room to improvement compared with Best-Found.

7.1Conclusion

On the Prophet, this paper proposes animportancedegree basedthreadpartitioningapproach (IDaTPA), to realize the partition of irregular programs. According to the characteristics of the program, the thread partition scheme is predicted, and the programs are partitioned according to the predicted partition scheme. Finally, Prophet simulator is used to verify its execution performance. The primary researches are listed as follows:

7.1.1Construction of sample set

This paper proposes to construct a TLS sample set, which is composed of feature and partition scheme. The features are extracted along the CFG’s speculative path in every program’s procedure. For the method of partitioning scheme’s acquisition in TLS samples, an expert experience based explicit partition is used to explicitly partition the program, and every procedure’s satisfied partition scheme in the program is calculated.

7.1.2Thread partition based on programs’ characteristics

This paper applies a KNN classification algorithm in machine learning to generating a better partition scheme for every procedure to be partitioned from the sample set.

7.1.3Prediction of partition scheme

This paper proposes an IDaTPA to partition programs with a KNN method. This paper performs nonloop partition and loop partition respectively. An evaluation model is established for the programs’ nonloops, and nonloops are iteratively partitioned in accordance with the evaluation model. For the loops, the inner loops and the nested iterations are respectively partitioned; for nested loops, nested iteration and sequential spawning are used for thread partitioning; for the inner loops, nonloop partitioning method is used for thread partitioning.

7.1.4Validation

Using Olden and SPEC2020 benchmarks, the effect of IDaTPA is verified on Prophet, and is compared to the original partition results. The test results indicate that the IDaTPA averagely delivers a speedup of 1.80 on a 4-core processor.

7.2Future work

TLS has been evolving many years, showing great advantages in making use of multicore resources. IDaTPA is proposed to handle the issue that conventional partitioning approaches can not generate the best partitioning scheme for unknown programs. The trend of thread partition falls on two points: 1. partition process will be facilitated on foundation of procedures of programs; 2. IDaTPA will be implemented on hardwares.

7.3Perspectives

As to the computing platform, the implementation of TLS is gradually turning from single chip multi-core component to distributed platform, like thread level speculative parallelism of decompression algorithm in [29] on Spark platform. To overcome the limitation that simulated annealing algorithm (SA) has low efficiency as the conventional SA algorithms still run on new platforms with low parallelism and the computing resource can not be utilized adequately, Wang et al. [30] expands the parallelism of algorithms to improve algorithms’ efficiency by raising a speculative parallel SA algorithm based on Apache Spark.