computer science

Mark–compact algorithm

Mark–compact algorithm is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Mark–compact algorithm rather than just read about it. In short: In computer science, a mark–compact algorithm is a type of garbage collection algorithm used to reclaim unreachable memory. Mark–compact algorithms can be regarded as a combination of the mark–sweep algorithm and Cheney's copying algorithm.

Key takeaways

Mark–compact algorithm belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Mark–compact algorithm to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Mark–compact algorithm from memory before moving on to harder problems.

Reference excerpt

In computer science, a mark–compact algorithm is a type of garbage collection algorithm used to reclaim unreachable memory. Mark–compact algorithms can be regarded as a combination of the mark–sweep algorithm and Cheney's copying algorithm. First, reachable objects are marked, then a compacting step relocates the reachable (marked) objects towards the beginning of the heap area. Compacting garbage collection is used by modern JVMs, Microsoft's Common Language Runtime and by the Glasgow Haskell Compiler.

Algorithms After marking the live objects in the heap in the same fashion as the mark–sweep algorithm, the heap will often be fragmented. The goal of mark–compact algorithms is to shift the live objects in memory together so the fragmentation is eliminated. The challenge is to correctly update all pointers to the moved objects, most of which will have new memory addresses after the compaction. The issue of handling pointer updates is handled in different ways.

Table-based compaction

A table-based algorithm was first described by Haddon and Waite in 1967. It preserves the relative placement of the live objects in the heap, and requires only a constant amount of overhead. Compaction proceeds from the bottom of the heap (low addresses) to the top (high addresses). As live (that is, marked) objects are encountered, they are moved to the first available low address, and a record is appended to a break table of relocation information. For each live object, a record in the break table consists of the object's original address before the compaction and the difference between the original address and the new address after compaction. The break table is stored in the heap that is being compacted, but in an area that is marked as unused. To ensure that compaction will always succeed, the minimum object size in the heap must be larger than or the same size as a break table record. As compaction progresses, relocated objects are copied towards the bottom of the heap. Eventually an object will need to be copied to the space occupied by the break table, which now must be relocated elsewhere. These movements of the break table, (called rolling the table by the authors) cause the relocation records to become disordered, requiring the break table to be sorted after the compaction is complete. The cost of sorting the break table is O(n log n), where n is the number of live objects that were found in the mark stage of the algorithm. Finally, the break table relocation records are used to adjust pointer fields inside the relocated objects. The live objects are examined for pointers, which can be looked up in the sorted break table of size n in O(log n) time if the break table is sorted, for a total running time of O(n log n). Pointers are then adjusted by the amount specified in the relocation table.

LISP 2 algorithm In order to avoid O(n log n) complexity, the LISP 2 algorithm uses three different passes over the heap. In addition, heap objects must have a separate forwarding pointer slot that is not used outside of garbage collection. After standard marking, the algorithm proceeds in the following three passes:

Compute the forwarding location for live objects. Keep track of a free and live pointer and initialize both to the start of heap. If the live pointer points to a live object, update that object's forwarding pointer to the current free pointer and increment the free pointer according to the object's size. Move the live pointer to the next object End when the live pointer reaches the end of heap. Update all pointers For each live object, update its pointers according to the forwarding pointers of the objects they point to. Move objects For each live object, move its data to its forwarding location. This algorithm is O(n) on the size of the heap; it has a better complexity than the table-based approach, but the table-based approach's n is the size of the used space only, not the entire heap space as in the LISP2 algorithm. However, the LISP2 algorithm is simpler to implement.

The Compressor The Compressor compaction algorithm has the lowest complexity among compaction algorithms known today. It extends IBM’s garbage collection for Java. The serial version of the Compressor maintains a relocation map that maps the old address of each object to its new address (i.e., its address before compaction is mapped to its address after compaction). In a first pass, the mapping is computed for all objects in the heap. In a second pass, each object is moved to its new location (compacted to the beginning of the heap), and all pointers within it are modified according to the relocation map. The computation of the relocation map in the first pass can be made very efficient by working with small tables that do not require a pass over the entire heap. This keeps the Compressor complexity low, involving one pass over small tables and one pass over the full heap. This represents the best-known complexity for compaction algorithms. The Compressor also has a parallel version in which multiple compacting threads can work together to compact all objects in parallel. The Compressor also has a concurrent version in which compacting threads can work concurrently with the program, carefully allowing the program to access objects as they are being moved towards the beginning of the heap. The parallel and concurrent versions of the Compressor make use of virtual memory primitives.

See also Dead-code elimination Tracing garbage collection

References

Worked examples

Example 1 — a first encounter with Mark–compact algorithm

Start with the simplest possible case. Write down what Mark–compact algorithm claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Mark–compact algorithm before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Mark–compact algorithm ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Mark–compact algorithm

In research: Mark–compact algorithm appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Mark–compact algorithm in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Mark–compact algorithm is common in secondary-school and first-year university syllabi. It links to neighbouring topics Automatic memory management, Memory management algorithms, so understanding it makes those chapters shorter.
In everyday life: Look for Mark–compact algorithm outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Mark–compact algorithm in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Mark–compact algorithm means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Mark–compact algorithm out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Mark–compact algorithm in simple terms?

Why does Mark–compact algorithm matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Mark–compact algorithm?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Mark–compact algorithm.