External Memory Pipelining Made Easy With TPIE

10/27/2017 ∙ by Lars Arge, et al. ∙ SCALGO 1

When handling large datasets that exceed the capacity of the main memory, movement of data between main memory and external memory (disk), rather than actual (CPU) computation time, is often the bottleneck in the computation. Since data is moved between disk and main memory in large contiguous blocks, this has led to the development of a large number of I/O-efficient algorithms that minimize the number of such block movements. TPIE is one of two major libraries that have been developed to support I/O-efficient algorithm implementations. TPIE provides an interface where list stream processing and sorting can be implemented in a simple and modular way without having to worry about memory management or block movement. However, if care is not taken, such streaming-based implementations can lead to practically inefficient algorithms since lists of data items are typically written to (and read from) disk between components. In this paper we present a major extension of the TPIE library that includes a pipelining framework that allows for practically efficient streaming-based implementations while minimizing I/O-overhead between streaming components. The framework pipelines streaming components to avoid I/Os between components, that is, it processes several components simultaneously while passing output from one component directly to the input of the next component in main memory. TPIE automatically determines which components to pipeline and performs the required main memory management, and the extension also includes support for parallelization of internal memory computation and progress tracking across an entire application. The extended library has already been used to evaluate I/O-efficient algorithms in the research literature and is heavily used in I/O-efficient commercial terrain processing applications by the Danish startup SCALGO.



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I Introduction

When handling large datasets that exceed the capacity of the main memory, movement of data between main memory and external memory (disk), rather than actual (CPU) computation time, is often the bottleneck in the computation. The reason for this is that disk access is orders of magnitude slower than internal memory access. Thus, since data is moved between disk and main memory in large contiguous blocks, it is often more important to design algorithms that minimize block movement than computation time when handling massive data. This has led to the development of a large number of I/O-efficient algorithms in the I/O-model by Aggarwal and Vitter [1]. In this model, the computer is equipped with a two-level memory hierarchy consisting of an internal memory capable of holding data items and an external memory of conceptually unlimited size. All computation has to happen on data in internal memory and data is transferred between internal and external memory in blocks of consecutive data items. Such a transfer is called an I/O-operation or I/O, and the cost of an algorithm is the number of I/Os it performs. The number of I/Os required to read or write items from disk is , while the number of I/Os required to sort items is  [1].

While many I/O-efficient algorithms have been developed in the I/O-model of computation, actually implementing these algorithms can be somewhat of a challenge since operating systems do not give complete control over movement of blocks and management of main memory. However, two major libraries TPIE [13] and STXXL [10] have been developed to support I/O-efficient algorithm implementations. It turns out that most I/O-efficient algorithms are naturally composed of components that stream through one or more lists of data items while producing one or more such output lists, or components that sort such lists. TPIE in particular uses this to provide an interface where list stream processing and sorting can be implemented in a simple and modular way without having to worry about memory management or block movement. However, if care is not taken, such a streaming-based implementation can lead to practically inefficient algorithms since lists of data items are typically written to (and read from) disk between components. In implementations consisting of many small (but I/O-efficient) components, the I/Os incurred when writing and reading such lists can easily comprise more than half of the total number of I/Os, and while this may not be a problem when considering asymptotic theoretical performance, it is unacceptable in practice when the total execution time is measured in hours or days.

In this paper we present a major extension of the TPIE library that includes a pipelining framework that allows for practically efficient streaming-based implementations while minimizing I/O-overhead between streaming components. The framework pipelines streaming components to avoid I/Os between components, that is, it processes several components simultaneously while passing output from one component directly to the input of the next component in main memory. TPIE automatically determines which components to pipeline and performs the required main memory management, and the extension also includes support for parallelization of internal memory computation and progress tracking across an entire application. Thus TPIE supports efficient streaming-based implementations of I/O-efficient algorithms, and TPIE applications are naturally implemented as reusable components, thereby reducing programming time and code duplication.

I-a Previous Work

As mentioned, two major software libraries support implementation of I/O-efficient algorithms for big data analysis, namely TPIE [13] and STXXL [10]. They are both C software libraries, and as opposed to many of the frameworks that have emerged for supporting big data analysis in the last decade, such as e.g. MapReduce [9], Spark [15], and Flink [3], they mainly support single-host implementations. One reason for this is that the libraries, in particular TPIE, are designed to support implementations on standard commodity hardware. Another reason is that no efficient distributed algorithms are known for many of the problems for which I/O-efficient algorithms have been studied and implemented; we refer to surveys [4, 14] and descriptions of implementations (e.g. [2, 5, 6, 11, 12]) for references. Thus in this paper we also focus on single-host implementations. However, is should be mentioned that in the context of distributed programming, pipelining has recently been studied with the Thrill framework [8].

Although both libraries for implementation of I/O-efficient algorithms, the overall philosophies of the TPIE and STXXL are somewhat different. The philosophy of TPIE (the Templated Portable I/O Environment) is to provide a high-level interface that allows for easy translation of abstract I/O-efficient algorithm descriptions into code that is portable across computational platforms and not unnecessarily complex. Thus building on the fact that most I/O-efficient algorithms are composed of streaming components, TPIE provides a generic stream interface that hides how blocked I/O is performed and instead provides methods for processing one data item at a time. TPIE also provides internal memory management where memory allocations are automatically counted towards an application-wide memory limit, and where an application can at any point determine the currently available main memory. Thus applications do not have to explicitly keep track of available memory, which often simplifies implementations considerably. For example, in the TPIE built-in streaming-based implementation of the I/O-optimal external multi-way merge-sort, the number of sorted streams that can be merged I/O-efficiently (without being swapped out by the operating system) depends on the available main memory, where care has to be taken to ensure that the memory used to hold blocks of items for each used stream is counted towards the amount of available memory. The TPIE memory management allows for determining the number of streams to merge without explicitly keeping track of available memory and memory used for blocked I/O. Overall, TPIE is designed to remove focus from the tedious details of creating I/O-efficient applications and allows for implementations that are efficient on all hardware platforms with minimal configuration.

The philosophy of STXXL (Standard Template library for XXL data sets) on the other hand is to achieve maximum I/O-throughput by reducing I/O-overhead as much as possible and by exposing the characteristics of the hardware to the application programmer. Thus, to avoid any overhead induced by the operating system, STXXL allows the user to configure separate disks for use with applications outside of the file systems of the operating system. In fact, STXXL project programmers recommend that a separate disk is set aside for STXXL applications. STXXL also explicitly supports parallel disks. Like TPIE, STXXL supports streaming-based implementations and includes various basic streaming components such as sorting, but unlike TPIE it actually contains support for pipelining of streaming components. However, STXXL expects the application programmer to explicitly define which components to pipeline and explicitly manage main memory. Thus, the programmer e.g. has to specify how much memory each streaming component in a pipelined application should use. A separate (not officially released) branch of STXXL contains support for utilizing multi-core processors for the internal-memory work of pipelined applications [7]. Overall, STXXL is designed such that an application can be tailored to the available hardware, and with the proper configuration an STXXL application can achieve close to full utilization of the available I/O bandwidth.

I-B Our Results

In this paper we present a major extension of the TPIE library that includes a pipelining framework that allows for practically efficient streaming-based implementations while minimizing I/O-overhead between streaming components. The extension also includes support for progress tracking across an entire application and for parallelization of internal memory computation.

Like STXXL, the TPIE pipelining framework saves I/Os by passing intermediate results between streaming components directly in main memory. However, TPIE pipelining is the first framework to provide automatic pipeline and memory management and thus combining the best of the TPIE and STXXL streaming philosophies. The framework is component-centric in that the memory requirement of each streaming component is specified locally by the component developer. The automatic pipeline and memory management then means that at runtime TPIE will automatically determine which components to pipeline and distribute memory among multiple components of a large application in a way that automatically uses all the main memory available to the application. Thus, unlike in STXXL, a TPIE programmer e.g. does not have to consider how the memory uses of the individual components have to be adjusted when they are combined into an application. While such adjustments, along with adjustments of the grouping of components into pipelines, can be done manually for small projects, it can be very cumbersome for large-scale professional software projects involving many programmers, where modification of a component to use more memory can very easily lead to memory over-usage problems if the memory use of other components are not adjusted accordingly. Thus the TPIE component-centric approach simplifies the application development process, promotes modularity and supports maintainability.

Since I/O-efficient applications are typically long-running processes that take hours or days to complete, it is important that an application is able to provide a progress bar that gives a precise estimate of the progress of its execution. To be able to do so in a simple way, the TPIE extension also takes a component-centric approach. Like for memory use, a component developer can in a simple way include support for information about the progress of the component, and TPIE automatically combines information from all components and thus supports a single progress bar that advances from 0% to 100% at a constant pace. Thus, again the use of a component-centric approach promotes modularity.

Especially after minimizing I/O, the use of multi-core parallelization can often help to bring down the running time of massive data applications. Thus the TPIE extension includes easy support for such parallelization by allowing the application programmer to wrap a part of a pipeline in a parallelization directive that will trivially parallelize it across all CPU cores. For instance, when forming sorted runs in multi-way merge sort, the internal memory sorting algorithm in TPIE automatically uses all the available CPU cores.

Overall, the major TPIE library extension presented in this paper supports efficient streaming-based implementations of I/O-efficient algorithm in a simple, modular and maintainable way, and I/O-efficient algorithms can thus be composed and adapted in commercial and research applications while dealing systematically with important aspects, such as memory management and progress tracking, that are not intrinsic to the algorithmically optimal solution. The extended library has already been used to evaluate I/O-efficient algorithms in the research literature (e.g. [5, 6]) and is heavily used in I/O-efficient commercial terrain processing applications by the Danish startup SCALGO111SCALGO: Scalable Algorithmics. https://scalgo.com. The extension is integrated into the official TPIE project that is available on GitHub as free and open source software222TPIE: Templated Portable I/O Environment. http://madalgo.au.dk/tpie.

The rest of the paper is structured as follows. In Section II, we motivate pipelining with a concrete algorithm based on scanning and sorting. After this motivation, we in Section III present in full generality how to use the TPIE extension and briefly discuss its implementation.

Fig. 1: Pipelined components in a real TPIE application developed by SCALGO. Dashed lines represent phase ordering dependencies resulting from blocking edges in the flow graph.

Ii An Example Problem

In this section we present an example of a typical sub-problem in an I/O-efficient application, and we show that the problem benefits from a pipelined implementation. By implementing every sub-problem in a bigger data processing application (such as the real-world example in Figure 1) using pipelining, more than half of the I/Os overall can be saved.

Ii-a The Raster Transformation Problem

In geographic information systems (GIS), a terrain is often represented as a raster of heights with each cell indicating the height of the terrain in a certain point. Since the Earth is spherical and a raster is flat, it is not possible to map the entire surface of the Earth continuously to a raster. However, if only a particular region, country or continent needs to be represented, it is possible to project the chosen region to a plane in a way that roughly maintains the geodesic distances and areas. When several rasters must be processed together they must be in the same projection.

We call the problem of transforming a raster from one projection to another the raster transformation problem. Essentially, the problem consists of projecting each cell of the raster from the source projection plane to the unit sphere and from the unit sphere to the target projection plane. These two steps can be represented by a function that maps each cell of the target raster projection to the corresponding cell of the source raster projection. Thus, in the raster transformation problem we are given an input raster of size (that is a by matrix of numbers) stored in row-major order, and we want to produce an output raster of size in row-major order such that the value of a cell in is copied from the value of a cell in . Below we for convenience let be the number of cells in both the input and output raster.

Fig. 2: Raster transformation algorithm. LABEL:sub@fig:algorithm-old The algorithm without pipelining, requiring reads and writes. LABEL:sub@fig:algorithm-new The algorithm with pipelining, requiring just reads and writes.

The raster transformation problem can easily be solved in optimal time simply by for each cell in reading the corresponding input value at in . However, this solution might be very I/O-inefficient. For example, if represents matrix transposition where then each access to requires a new block to be read (assuming ) and thus the solution performs I/Os in the worst case. For matrix transposition in particular, only I/Os are required [1]. In fact, in general the problem can be solved in I/Os using a simple five step streaming algorithm (refer to Figure (a)a): First a stream is constructed containing for each cell of an item consisting of a pair . Next is sorted such that appear in the same row-major order used to store . In the third step, and are then scanned simultaneously to construct a stream containing an item for each pair in where is the value of at position . Then is sorted into the row-major order used to store . In the fifth and final step, is then scanned and for each entry the value is output to . Since the algorithm performs a constant number of scanning and sorting steps it uses I/Os and can easily be implemented using the streaming support of either TPIE or STXXL. Refer to GitHub333https://github.com/Mortal/pipelining/blob/8996e5c87d/tpie_imperative/transform_paper.cpp#L60-L104 for a TPIE code example.

As discussed in the introduction, streaming-based implementations of even simple I/O-efficient algorithms, as the raster transformation algorithm above, might not be practically efficient because items are written to disk between steps. To illustrate this, we will analyze the exact number of items read and written by the above algorithm. For simplicity, we assume that elements can be sorted using reads and writes, which is a practically realistic assumption if external merge-sort is used. Recall that external merge-sort works by first scanning through the input elements and sorting elements at a time in internal memory to produce sorted runs. This requires reads and writes. Next the sorted runs are merged together at a time (using a block of internal memory for each run) to produce longer sorted runs, again using reads and writes. The merging process is repeated until a single sorted output is obtained. However, in practice (where and are on the order of , and , respectively) so only a single merging step is required. Using this, we can easily realize that the above raster transformation algorithm requires reads and writes without pipelining (refer again to Figure (a)a): Generating the stream in the first step requires writes, and sorting it in the second step requires reads and writes. Reading and simultaneously in the third step to produce requires reads and writes. Again, sorting in the fourth step requires reads and writes, and finally, reading and writing in the fifth step requires reads and writes. However, by modifying the five steps of the algorithm so that the intermediate result of one step is immediately used by the next step (if possible) without storing the intermediate result on disk, that is, by using pipelining, we can reduce the number of reads and writes to each. More precisely, we can save writes and reads of between step one and two by immediately producing the initial sorted runs of step two while performing step one. Similarly, we can save the writes and reads of between step two and three by performing step three (scanning and ) simultaneously with merging the sorted runs. Note that apart from the write and read between the run formation and merging in step two, we in this way avoid writing altogether. In a similar way, we can avoid writing and save reads and writes by also producing the initial sorted run of step four while performing step 3, as well as reads and writes by performing step five simultaneously with merging of the sorted runs in step 4. Altogether, we save reads and writes, that is, over half of the I/Os. Although this does not change the asymptotic I/O-complexity of the algorithm, it translates into a running time reduction of 22 hours if we assume an input size of 1 TB and a disk read/write speed of 100 MB/s.

Note that the pipelining process described above conceptually transforms the five-step algorithm into a three-phase algorithm as indicated in Figure (b)b, where e.g. the second phase consists of the merging part of the step two sorting, step three, and the run formation part of the step four sorting. One could of course implement the algorithm by implementing these three phases directly, that is, by implementing several special versions of external merge-sort (or rather, special run formation, merging, and merging-run formation implementations). However, this would not only be cumbersome, but also unacceptable from a software engineering point of view. Instead, direct support of pipelining where the five-step algorithm is automatically pipelined would be desirable. However, such a pipelining would require system support for identification of phases and careful memory management. For example, the merge and run formation parts of phase two of the three-phase algorithm would normally both require all of the main memory, so the memory somehow needs to be divided between the two parts. As described below, this is handled somewhat differently in STXXL and the new TPIE extension.

1void transform(raster_input & input, raster_output & output, size_t memory_available) {
2     // In phase 1, the single sorter can use all the available memory.
3    const size_t phase1_sort_memory = memory_available;
4    // In phase 2, the two sorters receive each half the available memory,
5    // excluding the memory used to store a single block from the input.
6    const size_t phase2_sort_memory = (memory_available - input.buffer_size()) / 2;
7    // In phase 3, there is a single sorter and an output buffer.
8    const size_t phase3_sort_memory = memory_available - output.buffer_size();
9     GenerateOutputPoints output_points(output.dimensions());
10    typedef TransformPoints<GenerateOutputPoints> TransformOutputPoints;
11    TransformOutputPoints point_pairs(std::move(output_points), input.dimensions());
12    typedef stxxl::stream::runs_creator<TransformOutputPoints, input_yorder> rc_type;
13    rc_type rc(std::move(point_pairs), input_yorder(), phase1_sort_memory);
14    typedef stxxl::stream::runs_merger<rc_type::sorted_runs_type, input_yorder> rm_type;
15    // The following call to rc.result() executes the first phase.
16    rm_type rm(rc.result(), input_yorder(), phase2_sort_memory);
17    RasterReader input_raster_reader(input);
18    typedef PointFiller<rm_type, RasterReader> FillOutputPoints;
19    FillOutputPoints filler(std::move(rm), std::move(input_raster_reader));
20    typedef stxxl::stream::runs_creator<FillOutputPoints, point::yorder> rc2_type;
21    rc2_type rc2(std::move(filler), point::yorder(), phase2_sort_memory);
22    typedef stxxl::stream::runs_merger<rc2_type::sorted_runs_type, point::yorder> rm2_type;
23    // The following call to rc2.result() executes the second phase.
24    rm2_type rm2(rc2.result(), point::yorder(), phase3_sort_memory);
25    // The following call to write_raster() executes the third phase.
26    write_raster(std::move(rm2), output);
Fig. 3: Raster transformation using the STXXL streaming layer.

Ii-B STXXL Implementation

When implementing the raster transformation algorithm with pipelining using the STXXL streaming layer, the five steps of the algorithm are implemented individually as is natural from a software engineering point of view; we call each such individual part of a pipeline a component. However, since STXXL does not handle memory management, the implementation that combines the components then has to identify the three phases of the algorithm explicitly and compute how much memory is allocated to each of the components of a phase. Refer to Figure 3 for STXXL code that implements this, that is, the main code that implements the five step algorithm in three phases (excluding the code for the individual components). The code illustrates how three phases are explicitly identified and memory allocated. For example, in phase two the memory available for the two sorting components (merging of step two and run formation of step four) is computed by setting aside a buffer of size of the available main memory for reading the input, and then share the remaining memory between the two sorting components. Concretely, the computation is performed with the statement: sort_memory = (memory_available - block_size) / 2. While identifying phases and allocating memory in this way is easy in our simple example algorithm, it is more difficult in larger applications such as the example given in Figure 1. Especially if more than one programmer is working on the application, it is difficult and error-prone to distribute memory correctly.

Apart from the complexity that the need for phase identification and memory allocation adds to pipelined STXXL code, there are also some C syntax issues that add to the code complexity. More precisely, the C syntax used is quite verbose since for technical reasons names of the components often need to be repeated. The reason is that STXXL combines pipelining components using a C feature known as template instantiation that allows for the compiler to inline function calls between different components of the pipeline. For performance reasons, this is necessary when many small components are pipelined. However, the template instantiation syntax is not well-suited for use in large pipelined applications. As an example, consider the C statement typedef TransformPoints<GenerateOutputPoints> TransformOutputPoints; in the STXXL implementation of the raster transformation algorithm. In this statement, the C language typedef statement is used to declare TransformOutputPoints to be a type alias for the TransformPoints component instantiated with the GenerateOutputPoints component. Such a type alias is needed for each component of the pipeline, and only when all the type aliases have been defined the individual component objects can be declared and constructed. In this way, the pipeline has to be defined both in terms of type aliases nested within each other and as actual component objects combined together.

Ii-C TPIE Implementation Using Pipelining

As in the case of pipelined STXXL, in the implementation of the five step raster transformation algorithm using the extended TPIE library with pipelining, the components of the pipeline are implemented individually. However unlike in the STXXL implementation, the combination of the components becomes very simple since TPIE is component-centric and automatically identifies phases and performs memory management. To illustrate this, a diagram showing the eight components used to implement the five steps is given in Figure (a)a along with the pipelining code in Figure (b)b. Note how the code in Figure (b)b lines LABEL:line:tp-def-begin-LABEL:line:tp-def-end naturally corresponds to the pipeline in Figure (a)a. Note that the reading and writing of rasters are handled by two special components to separate the handling of specific raster formats from the algorithm, and how the two sorting components are implemented using two different built-in TPIE sorting components defined in lines LABEL:line:tp-sort1 and LABEL:line:tp-sort2 on Figure (b)b. The reason two different sorter implementations are used (and that the pipeline is defined in two statements defining p1 and p2, respectively) is that the output from the component sorting has to be read by the component constructing simultaneously with the output from the component reading the input raster . Thus, the component constructing has to control when data is received from the sorting component which is done through pull-based streaming. This functionality is implemented with a TPIE so-called passive sorter with an input and an output part defined in line LABEL:line:tp-sort1. On the other hand, the component sorting is a more traditional pipelined component that uses push-based streaming, where input data is received from preceding component (in this case the component constructing ) when ready, and output data in turn pushed to the subsequent component. It is defined with an ordinary TPIE sorter in line LABEL:line:tp-sort2. As an example of a component implementation, the code implementing the component generate_output_points is given in Figure 5. The component contains a method propagate() that is called by TPIE when setting up the pipeline, and a method go() that is called by TPIE to execute the actual component. The component also uses push-based streaming, and it pushes each produced element to the next component by calling the push() method of that component. The details of how the push and pull mechanisms work will be discussed in Section III, where the full TPIE pipelining framework and its implementation is described. Below we highlight some of the other framework features that are used in the raster transformation example.

Fig. 4: Raster transformation algorithm. LABEL:sub@fig:project-pipeline Pipeline illustrated as components. LABEL:sub@fig:project-code TPIE code implementing the pipeline.
1template <typename dest_t>
2struct GenerateOutputPoints : public tpie::pipelining::node {
3    GenerateOutputPoints(dest_t d): dest(std::move(d)) {}
4    virtual void propagate() override {
5        dimensions = fetch<rastersize_t>("outputsize");
6        set_steps(dimensions.width * dimensions.height);
7    }
8    virtual void go() override {
9        for (int y = 0; y < dimensions.height; y++)
10            for (int x = 0; x < dimensions.width; x++)
11                { step(); dest.push(point(x, y)); }
12    }
13    rastersize_t dimensions; dest_t dest;
15typedef tp::pipe_begin<tp::factory<GenerateOutputPoints>>
16    generate_output_points;
Fig. 5: generate_output_points component used in the TPIE raster transformation algorithm.

Memory management. As mentioned, TPIE automatically manages memory and divides available memory among components in a pipeline. Thus in the pipeline definition in Figure (b)b there is no code at all dealing with memory distribution. Often many components use only a small amount of static memory, whereas components such as sorting require dynamically allocated memory depending on the amount of available memory. In the latter case the component has to specify its minimum and maximum memory requirements in its implementation. In the example component shown in Figure 5 no requirement is specified since only static memory is used.

Metadata. Often many components in a pipeline need some sort of metadata about the items that are being streamed between components. In the example, certain components need to use the dimensions of the input and output rasters. While such metadata can of course be passed as parameters to the individual components in the pipeline definition, doing so makes the definition needlessly cluttered. Instead, TPIE provides a general facility for passing metadata between pipeline components. Thus, in Figure (b)b lines LABEL:line:tp-forward-begin-LABEL:line:tp-forward-end, the pipeline definition uses forward() to pass the dimensions of the input and output rasters to the components that need them. The individual components can then obtain the metadata using fetch(), such as when the component generate_output_points in Figure 5 line 5 retrieves the dimensions of the output raster.

Progress reporting. In the example, the TPIE support for progress reporting (e.g. as a progress bar) is also used. As with memory requirements, the code required to supply progress information is not part of the code in Figure (b)b where the pipeline is defined, but rather part of the implementation of the individual component. Thus, the component in Figure 5 provides the needed information by using set_steps() in line 5 in the propagate() method to define how many items it will produce, and then calling a progress stepping function in line 5 in the go() method for each item that it produces. When executing the pipeline in line LABEL:line:tp-exec of Figure (b)b the argument pi is a reference to a progress indicator object that tells TPIE how to display progress. To provide accurate progress estimations, TPIE actually uses statistical information about progress of previous runs of the code. To store information about runs, the problem’s instance size n, computed in line LABEL:line:tp-n, as well as a symbol TPIE_FSI used to identify the application being executed, are also passed to the TPIE framework when executing the pipeline in line LABEL:line:tp-exec.

Parallelism. The example takes advantage of TPIE support for multi-core CPU parallelism in two ways. First, the sorter components automatically parallelized in the run-formation phase. Second, wrapping the component compute_transformation in the directive tpie::pipelining::parallel(…) in line LABEL:line:tp-parallel makes TPIE automatically distribute this part of the computation among all available CPU cores.

Iii TPIE Pipelining

In this section we describe the TPIE pipelining framework in more detail. First in Section III-A we describe how to use the framework, and then in Section III-B we discuss some aspects of the implementation of the framework.

Iii-a Pipelining Use

In this section we first describe how a TPIE pipeline consisting of a number of components can be modeled using a so-called flow graph, and how this graph can be used to identify pipeline phases. Then we describe how components are implemented. Finally, we describe how a TPIE pipeline is constructed and executed.

Flow graph and phase identification. As described in Section II-C, a TPIE pipeline consists of a number of components that push data to or pull data from other components. We distinguish between two types of components, namely regular components that produce output as the input is processed and blocking components that have to process all input before producing any output. Blocking components consist of two sub-components, namely an input and an output sub-component, where the input sub-component processes all input before the output sub-component is invoked to produce the output; the input sub-component might store intermediate results on disk. Sorting is an example of a blocking component; the input sub-component naturally corresponds to the initial run formation step and the output sub-component to the merging step. Blocking components introduce the need for pipeline phases that are executed independently, since the input and output sub-components cannot be executed simultaneously. In the raster transformation algorithm in Section II, the three phases are exactly needed due to the two blocking sorting components.

A pipeline can conveniently be represented by a directed acyclic flow graph, with regular nodes corresponding to regular components and input nodes and output nodes corresponding to the sub-components of blocking components. Regular nodes are connected with other regular nodes and input and output nodes by edges directed along the streaming direction and labeled as push edges or pull edges in a natural way; note that a node cannot have both an outgoing push and an outgoing pull edge. Each input node is also connected with a directed blocking edge to the corresponding output node. To be able to automatically determine the phases of a pipeline, TPIE requires that the flow graph corresponding to the pipeline has two particular properties: First, if all blocking edges are removed, then no input and output nodes corresponding to the same blocking component should be in the same connected component. Second, if all push and pull edges are contracted, then the graph should be acyclic. The first property means that the connected components directly identify the pipeline phases that need to be executed independently. When each such connected component is contracted, each directed edge in the resulting graph indicates that the phase corresponding to needs to be executed before the phase corresponding to . Thus the second property ensures that there exists a valid (topological) order in which to execute the components.

While the second flow graph property above has to be fulfilled for any pipelined program to be valid, the first property only has to be fulfilled if we require that the program is constructed such that all but blocking components can be pipelined, that is, that streaming items are only written to disk by blocking components. For example, consider the small pipeline shown on the right, where component pushes to both a sorter and to a component , which in turn pulls output from the sorter. In this case, , and

the input and output nodes of the sorter are all in the same connected component in the phase graph without blocking edges. However, it is obviously not possible to pipeline all the components in one phase. In particular, it is not possible to pipeline and , since the output from the sorter used in is not available at the same time as the output from also used in . To remedy this problem, and make the flow graph fulfill the second property, a simple blocking component that delays the stream of items from to , by writing them temporarily to disk, can be inserted between and in the pipeline (such that the example has two phases). To support this, TPIE not only contains a built-in sorting blocking component, but also blocking components that delay and reverse a stream. Each of these components come in an active and a passive variant. In the active variant both the input and output sub-components use push-based streaming, and in the passive variant the input sub-component is push-based and the output sub-component pull-based.

Component implementation. The interface of each component in a TPIE pipeline must have certain methods; sub-components are essentially like regular components, so when we refer to components below we mean regular components and sub-components.

Methods push(), pull(), and can_pull() are used to stream data items between components. Consider a component corresponding to a node in the flow graph that produces data that is processed by a component corresponding to a node in the graph, that is, where there is an edge . With a slight abuse of notation, we use and to refer to the two components. If the edge is a push edge we say that is a destination of , and then must implement a push() method and must push each item in the stream to by calling the method .push(). If on the other hand the edge is a pull edge we say that is a source of , and then must implement a pull() method and must pull each item from by calling the method .pull(); must also implement the method can_pull() to return true if there is more data to pull and false otherwise.

A component that is neither the destination or the source of any other component must implement a go() method that repeatedly pushes or pulls data until there is no more data to process. This is because data is neither pushed to or pulled from by other components calling .push() or .pull(). Thus the go() method is used to start the execution of a phase.

Each component must implement (possibly empty) begin() and end() methods that are called before and after the stream processing of the phase containing , respectively. These methods can for example be used to allocate and deallocate memory used by , or set up data structures needed by . Component is also allowed to push items to its destinations and pull items from its sources in begin() and end(). This is e.g. useful when buffers need to be filled up at the beginning or emptied at the end of a phase.

Each component must also implement a (possibly empty) propagate() method that is also called before any stream processing in the phase containing and used to pass metadata between components. Inside the propagate() method may use the forward function to pass key-value pairs to components that can be reached from in the flow graph. It may use the fetch function to retrieve named metadata from other components. Often metadata includes information about input and output data size, and if TPIE should provide progress reporting, at least one component in each phase should provide progress information by calling the function set_steps() inside the propagate() method to indicate the number of items that it will process, and then call the step() function once for each item that is processed. Often is a node with no incoming push or pull edges in the flow graph, that is, the node that creates streaming data.

Finally, a component that requires dynamically allocated memory to perform its stream processing needs to indicate this to TPIE by calling the functions set_minimum_memory() and set_maximum_memory() in its class constructor to request between and bytes of memory, where is used to indicate that the component requests as much memory as possible. After TPIE has distributed memory, can then obtain information about how much memory it was assigned between and by calling the function get_available_memory() in the begin() method.

Pipeline construction and execution. After defining the pipelining components, a pipeline is constructed by stringing together components using the so-called pipe operator as in the expression p = generate_output_points() |compute_transformation() |sort_S1.input().memory(2) where three components, generate_output_points(), compute_transformation() and the input sub-component of sort_S1 are pipelined. For each component, as for the sort_S1.input() component in the example, one can set a memory priority using memory() to indicate to TPIE how important it is to allocate memory to the component; by default the priority is one, and a priority of means that if several components all request as much memory as possible using set_maximum_memory() then a component with priority will receive times the amount of memory as one with priority one.

To execute the pipeline one simply calls the object . TPIE then builds the flow graph and computes connected components to identify phases, and then contracts the components and topologically sorts the graph to find the order in which to execute the phases. After this TPIE executes each phase in turn. To execute a phase, TPIE first distributes memory to each component in the phase based on the memory requests and priorities, and then it calls the methods propagate(), begin(), go() and end() on the components in a specific order based on the flow graph. First, propagate() is called on the components in the phase in topological order to allow each component to call forward(), fetch() and set_steps(). The topological order is used since a component has to be able to pass metadata to components reachable from in the flow graph. Next begin() is called on all components in the order obtained by topologically sorting the flow graph where all push edges have been reversed; this topological order exists as the graph is acyclic, since a node in the flow graph cannot have both an outgoing push and an outgoing pull edge. This particular topological order is used since for a push edge , should be able to push to in .begin(), so .begin() should be called after .begin(); similarly, if is a pull edge, then .begin() must be called after .begin() is called. After this initialization, the main streaming part of the phase is executed by calling the go() method on the appropriate component. Finally, at the end of the phase end() is called on the components in reverse order of the begin() order, that is, in reverse topological order.

Iii-B Pipelining Implementation

Above we have already discussed how TPIE identifies and executes phases of a pipeline, and due to space constraints we cannot describe the entire implementation of TPIE pipelining in detail. In this section we therefore briefly discuss a few aspects of the implementation not described above. The interested reader is also referred to the technical documentation of TPIE .

Memory management. As mentioned, TPIE distributes memory to components in a phase based on the minimum and maximum memory requirements, along with the memory priority of each component in the phase. Component is assigned bytes of memory, for a value of such that the total assigned memory is smaller than the available memory. This way memory is distributed proportionally to the memory priorities unless this gives an amount of memory outside the interval. Since is a non-decreasing function of , TPIE can use binary search to find . Refer to Figure 6 for an example.

Progress reporting. TPIE also supports progress reporting for a pipeline. To ensure that a progress bar shown to the user progresses from 0% to 100% at a constant pace, TPIE maintains an execution time database with information about how large a fraction of the execution time was spent in each phase in the execution of the pipeline that processed the largest instance size. In order to distinguish between different pipelines in the execution time database, each pipeline execution carries the preprocessor macro TPIE_FSI as an argument, which is expanded by the compiler into a string that uniquely identifies the location in the code where the pipeline is defined. In this way, TPIE can store information about multiple pipelines in the execution time database.

Automatic parallelization. Automatic multi-core CPU parallelism is supported in TPIE by applying the tpie::pipelining::parallel(…) directive to a push-based pipeline component. In this case, the processing of the component is distributed among the available CPU cores by instantiating a copy of the component for each core and passing items to these components in a round-robin fashion as they arrive. To amortize the overhead of thread synchronization, items are passed to components in batches of items at a time.

Function inlining. To minimize the computational overhead of the many push() and pull() function calls required when executing a pipeline, TPIE is designed to allow the compiler to inline the processing of several consecutive pipelining components into one function. As in STXXL, this is achieved using template instantiations in C. Thus, if pushes to in the pipeline, then when is compiled the type of is known to the compiler so the implementation of .push() can be inlined into . However unlike STXXL, TPIE is designed to hide the resulting complex type definitions from the pipeline definition, and instead of using recursive template instantiations the pipe operator can be used to define a TPIE pipeline. This is very convenient when building large pipelined applications.

Node Minimum Maximum Priority Assigned
Fig. 6: Memory assignment for four nodes where , . Nodes and are assigned times their priority, whereas nodes and are assigned their minimum and maximum memory, respectively.


  • [1] Alok Aggarwal and Jeffrey Scott Vitter. The input/output complexity of sorting and related problems. Communications of the ACM, pages 1116–1127, 1988.
  • [2] Deepak Ajwani, Roman Dementiev, Ulrich Meyer, and Vitaly Osipov. Breadth first search on massive graphs. In 9th DIMACS Implementation Challenge Workshop: Shortest Paths, 2006.
  • [3] Alexander Alexandrov, Rico Bergmann, Stephan Ewen, Johann-Christoph Freytag, Fabian Hueske, Arvid Heise, Odej Kao, Marcus Leich, Ulf Leser, Volker Markl, et al. The stratosphere platform for big data analytics. The VLDB Journal, 23(6):939–964, 2014.
  • [4] Lars Arge. External Memory Data Structures, pages 313–357. Springer US, Boston, MA, 2002.
  • [5] Lars Arge, Gerth Stølting Brodal, Jakob Truelsen, and Constantinos Tsirogiannis. An optimal and practical cache-oblivious algorithm for computing multiresolution rasters. In Proc. 21st European Symp. Alg., 2013.
  • [6] Lars Arge, Jakob Truelsen, and Jungwoo Yang. Simplifying massive planar subdivisions. In Proc. 16th Workshop on Alg. Eng. Exp., pages 20–30. SIAM, 2014.
  • [7] Andreas Beckmann, Roman Dementiev, and Johannes Singler. Building a parallel pipelined external memory algorithm library. Proc. 2009 Intl. Parallel and Distributed Processing Symp., 2009.
  • [8] Timo Bingmann, Michael Axtmann, Emanuel Jöbstl, Sebastian Lamm, Huyen Chau Nguyen, Alexander Noe, Sebastian Schlag, Matthias Stumpp, Tobias Sturm, and Peter Sanders. Thrill: High-performance algorithmic distributed batch data processing with C. In 2016 IEEE Intl. Conf. Big Data, pages 172–183. IEEE, 2016.
  • [9] Jeffrey Dean and Sanjay Ghemawat. Mapreduce: simplified data processing on large clusters. Comm. ACM, 51(1):107–113, 2008.
  • [10] Roman Dementiev, Lutz Kettner, and Peter Sanders. STXXL: standard template library for XXL data sets. Softw., Pract. Exper., 38(6):589–637, 2008.
  • [11] Roman Dementiev, Peter Sanders, Dominik Schultes, and Jop Sibeyn. Engineering an external memory minimum spanning tree algorithm. In Exploring New Frontiers of Theoretical Informatics, pages 195–208. Springer, 2004.
  • [12] Ulrich Meyer and Vitaly Osipov. Design and implementation of a practical I/O-efficient shortest paths algorithm. In Proc. Meeting on Alg. Eng. Exp., pages 85–96. Society for Industrial and Applied Mathematics, 2009.
  • [13] Thomas Mølhave. Using TPIE for processing massive data sets in C. SIGSPATIAL Special, 4(2):24–27, 2012.
  • [14] Jeffrey Scott Vitter. External memory algorithms and data structures: Dealing with massive data. ACM Computing surveys (CSUR), 33(2):209–271, 2001.
  • [15] Matei Zaharia, Mosharaf Chowdhury, Michael J Franklin, Scott Shenker, and Ion Stoica. Spark: Cluster computing with working sets. HotCloud, 10(10-10):95, 2010.