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A context switch transfers CPU execution from one operating-system process or software thread to another while preserving enough execution context for the suspended task to resume. The operating-system kernel saves the outgoing task's processor registers, including its resume program counter and stack pointer, and restores the incoming task's saved state. Hardware entry and software switching divide this work differently across architectures; the diagram groups them into logical save and load steps. During the transition the same CPU executes the operating-system kernel's scheduler and switching code. ^[ref]

For a schematic switch from A to B, let A's resume program counter be $p_A$, its stack pointer $u_A$, and one illustrative data processor register contain 7. B's saved values are $p_B,u_B,42$. Saving A retains $(p_A,u_A,7)$ in A's execution context; loading B establishes $(p_B,u_B,42)$ before B resumes. These are selected fields, not a complete architecture-specific execution context. Later restoring A recovers its 7 even though B has used the same physical processor register for 42.

The useful invariant is that each suspended task retains its own resume state, so another task's use of the processor registers does not erase it. This is what makes repeated turns under round-robin scheduling possible. Saving an execution context does not copy the entire address space. Switching between software threads of one operating-system process can retain the same address space; switching between operating-system processes with different address spaces may also require changing the memory management unit's translation context, such as the active page table and associated address space identifier, depending on the architecture. Their contents remain in computer memory. ^[ref]

Entering the operating-system kernel through an interrupt, synchronous processor exception or system call does not automatically imply a context switch to another task. The handler may return to the same task; process scheduling determines whether another runnable task should take over. A context switch consumes time, and a changed memory cache or translation lookaside buffer working environment can add subsequent costs. Those costs are excluded from the toy process scheduling timelines, which are calculations rather than operating system benchmarks. ^[ref][ref]

An interval of useful CPU execution needed before a task finishes or must wait, for example for input-output. A real operating-system process can need many CPU bursts; the example under process scheduling assigns each task just one CPU burst, possibly split across several turns by round-robin scheduling.

The operating-system kernel is the core of an operating system, controlling access to resources and supplying mechanisms used by operating-system processes. Processor privilege levels let it enforce boundaries that ordinary programs cannot simply overwrite. The operating-system kernel connects process scheduling, virtual memory and file system operations; a system call requests a service across that boundary. A monolithic kernel and a microkernel divide services differently, so the location of every file system service is a design choice.

For the toy file system, opening a resolves its directory entry to inode 42, establishes an open file description, and returns file descriptor 3. Later reads use that file descriptor to reach the existing open file description, without resolving the spelling a again. This explains how unlinking an open file preserves access: the open reference survives the directory entry. The per-operating-system process table interpreting 3 is maintained by the operating-system kernel; 3 itself is the small integer passed by the program, not inode 42. ^[ref]

Keeping several operating-system processes available so process scheduling can run another when one waits. Increasing their number can increase the combined working set demand beyond the available page frames, producing memory thrashing rather than more useful CPU work.

An operating-system process is an execution instance of a program, with an address space, one or more software threads carrying execution contexts, and operating system resources such as file descriptors. A program describes instructions; an executable file stores a form that can be loaded for execution. Starting the same executable twice can create two operating-system processes, each with its own evolving state. A operating-system process can be waiting for input-output or waiting for a turn on the CPU, rather than executing at every instant. ^[ref]

For example, start the same counter program as operating-system processes A and B. In this toy mapping, both use virtual address 0x1234 for a counter x, but A reads x = 7 while B reads x = 42. The number 0x1234 is interpreted through the current address space, so it need not denote the same contents in both operating-system processes. Writing A's ordinary private counter does not change B's counter. Compare virtual memory for an explicit mapping to different physical addresses.

Separate virtual memory address spaces provide a usual default isolation boundary, not a claim that all computer memory is universally private. Deliberately shared mappings let operating-system processes communicate through the same stored data. Even logically private contents need not occupy distinct page frames at every moment: read-only program code can be shared, and copy-on-write can keep a common memory page until a write requires a private copy. The distinction is between which accesses and changes are permitted and which physical storage is currently reused. ^[ref][ref]

This abstraction lets independent activities reuse the same executable while keeping their changing state separate. Process scheduling distributes CPU time; virtual memory interprets and protects addresses; file descriptors connect an execution to open resources. The useful design is controlled independence with explicit ways to share. Creating operating-system processes, maintaining their mappings and performing context switches require time and computer memory; isolation alone gives no universal performance guarantee.

Process scheduling chooses which runnable operating-system process or software thread uses a CPU next, and when it gives up that CPU. Runnable means able to execute now; work waiting for an input-output operation is not eligible until that wait ends. A ready queue records eligible work. The policy chooses a recipient; a context switch supplies the mechanism for transferring execution. Sharing a single CPU lets a long calculation and an interactive program both make progress, although only one execution stream runs on that CPU at an instant.

Consider three single-software thread operating-system processes on one CPU, with abstract time units, zero context switch or scheduler overhead, and no input-output blocking. Their arrival times and CPU bursts are $(a_A,b_A)=(0,5)$, $(a_B,b_B)=(1,3)$ and $(a_C,b_C)=(2,1)$. Each CPU burst is all the execution that its task needs. With FCFS, the current task runs to completion: A occupies $[0,5)$, B $[5,8)$ and C $[8,9)$. With round-robin scheduling and scheduling quantum $q=2$, the intervals are A $[0,2)$, B $[2,4)$, C $[4,5)$, A $[5,7)$, B $[7,8)$ and A $[8,9)$. At a boundary, arrivals join the ready queue before the unfinished task whose scheduling quantum has expired; this fixes the otherwise ambiguous ordering at time 2.

The following triples always list A, B, C. Completion is an absolute time; scheduling turnaround time, scheduling waiting time and scheduling response time are durations measured from the relevant events.

For example, B first runs at time 2 under round-robin scheduling, so its scheduling response time is $2-1=1$. It finishes at time 8, giving scheduling turnaround time $8-1=7$ and scheduling waiting time $7-3=4$. Under FCFS, C waits $8-2=6$ before its sole execution interval. Under round-robin scheduling, A waits during $[2,5)$ and $[7,8)$, totaling $3+1=4$. Both timelines do exactly $5+3+1=9$ units of useful CPU work; changing the order changes who benefits first.

The appeal is that shared progress can improve responsiveness: B and C get their first CPU service sooner under round-robin scheduling, while A completes later. The arithmetic mean of the scheduling response times falls from $10/3$ to $1$; the arithmetic mean of the scheduling turnaround times is $19/3$ for both policies in this particular example. These are different goals, and this workload establishes no universal ranking. Giving each continuously runnable task repeated turns is a practical form of fairness. A smaller scheduling quantum can shorten waits between turns, but makes context switches more frequent when competitors remain runnable. If each full slice performs $q$ units of useful work and incurs a fixed switching cost $s$, the overhead fraction in that simplified repeated cycle is $s/(q+s)$. Real costs and workloads determine the choice. ^[ref]

Serve the front of a FIFO queue of runnable tasks for at most one scheduling quantum. If the task finishes or must wait for input-output, it leaves the CPU earlier; if it remains runnable after consuming its allowance, append it to the rear of the ready queue. In the example under process scheduling, the ready queue at time 2 is B, C, A: B arrived at time 1, C joins at the boundary, then A is requeued. That one convention determines the subsequent order.

The delay from a task's arrival $a_i$ until its first execution $f_i$: $R_i=f_i-a_i$. This measures access to initial CPU service, rather than completion of an entire user request. For C under round-robin scheduling in process scheduling, $R_C=4-2=2$; its scheduling turnaround time is instead $5-2=3$.

The duration from a task's arrival $a_i$ until its completion $c_i$: $T_i=c_i-a_i$. It includes execution and all intervening waits. For A under round-robin scheduling in process scheduling, $T_A=9-0=9$, despite a scheduling response time of zero.

The total duration a task spends runnable but waiting for a CPU, including later waits after scheduling preemption. Under the zero-overhead, no-input-output-blocking assumptions of process scheduling, elapsed time partitions into useful execution and such waiting, so $W_i=T_i-b_i$, where $T_i$ is scheduling turnaround time and $b_i$ is the total CPU burst. Thus C under round-robin scheduling has $W_C=3-1=2$. If a task also spends time blocked on input-output, that blocked duration belongs to scheduling turnaround time but must not be counted as scheduling waiting time.

// Target: computer-science.bigb

A software thread is an execution stream within a operating-system process. Each software thread has its own execution context, including processor registers, an instruction pointer, and a call stack selected by its stack pointer. Software threads in the same operating-system process share its address space, global variables, dynamically allocated storage (the memory heap), and resources such as file descriptors. Separate register states let two software threads pause at different instructions and later resume correctly. A context switch saves and restores those states; it does not copy the entire shared address space. ^[ref][ref]

In the right half of the diagram under operating-system process, T1 and T2 access the same counter x at virtual address 0x1234. Their call stacks occupy separate regions of the same address space, and their saved processor registers can contain different values. A software thread's own call stack means the stack used for its calls, not a hardware protection boundary against another software thread. Another software thread with a suitable address can access that stack's live data, subject to the program's lifetime and synchronization rules. Sharing an address space enables easy communication and makes accidental interference possible.

Concurrency means that several activities can make progress over overlapping intervals; parallel execution means that several activities execute at the same instant. On one CPU execution slot, process scheduling can interleave software threads without simultaneous execution. Multiple processor cores can run eligible software threads in parallel, but merely creating software threads does not ensure parallel execution or a speedup.

Consider an explicitly abstract interleaving model: shared x starts at $0$, and each of T1 and T2 performs three indivisible logical steps in its own order: load x into its own register r, add $1$ to r, then store r into x. Each load sees the latest preceding store in the displayed order. These model assumptions specify the example; they do not describe every programming language or every hardware memory model. In serial order T1 finishes all three steps before T2 starts, so T2 loads $1$ and stores $2$.

An overlapping order instead gives T1 load $0$, T2 load $0$, T1 add to get r1 = 1, T2 add to get r2 = 1, T1 store $1$, and T2 store $1$. The load/store subsequence is therefore T1 load $0$, T2 load $0$, T1 store $1$, T2 store $1$: final $x=1$, although each software thread incremented its register correctly. The second store replaces the first with the same value, losing one intended update. This race condition is possible even with interleaving on a single CPU.

This is not an unprotected C implementation example. Conflicting unsynchronized accesses to an ordinary non-atomic C object from different software threads can constitute a data race, which has undefined behavior; the abstract trace is not a prediction of that program's allowed outcomes. ^[ref]

The repair explains why synchronization matters: mutual exclusion around the whole load/add/store sequence prevents the other software thread from starting an increment halfway through it. Alternatively, an appropriate atomic operation can make the complete increment one indivisible read-modify-write. In the toy model, either repair makes the two increments take effect in an order, producing $0+1+1=2$. Making only each load and each store an atomic operation still permits the displayed lost update. ^[ref]

The attraction is independently progressing activities with direct access to shared data: a user-interface software thread can handle input while another waits for input-output, and workers can divide independent pieces of a large calculation among available cores. The first benefit can exist on one CPU; the second requires suitable work and execution resources. Context switches, synchronization, register-state records and extra call stacks consume time or computer memory, so the useful partition depends on the workload. ^[ref]

An operating-system process's working set is the set of distinct memory pages used in a declared recent window. Here the window counts memory references, not clock time: let $r_i$ identify the memory page accessed by the $i$th memory reference, and let the positive integer $\Delta$ be the window length. Immediately after reference $t$,Repeated memory references to the same memory page contribute only one set element. The working set size is the cardinality $|W(t,\Delta )|$. This recent footprint estimates current demand; it cannot guarantee which memory pages future memory references will need. The resident set instead records which memory pages are actually present in computer memory.

For the 20-reference sequence in memory thrashing and $\Delta =4$, the successive working sets begin $\{1\}$, $\{1,2\}$, $\{1,2,3\}$ and $\{1,2,3,4\}$. At $t=5$, the window is references 2–5, namely $2,3,4,1$, so $W(5,4)=\{1,2,3,4\}$ again. Every later four-reference window contains all four memory pages. Under three-page frame LRU replacement, however, the resident set at $t=5$ is $\{1,3,4\}$: page 2 was evicted. Consequently the working set size is four even though only three memory pages are resident. Under four-page frame LRU replacement, the resident set contains the complete working set after reference 4.

Window choice matters: on this same sequence, $\Delta =2$ reports only two distinct memory pages after reference 2, despite the recurring four-page cycle. A short window forgets useful memory pages sooner; a long window retains evidence from older phases. Enlarging the window can only enlarge or preserve the working set at fixed $t$, because the shorter window is contained in the longer one. No single window is best for every workload.

For a concrete multiprogramming budget, suppose three operating-system processes each have a four-page working set, no shared page frames, and only eight page frames are available for their data. Their combined demand is 12 page frames; all three working sets cannot be resident together. Letting two operating-system processes run while the third waits makes their eight-page demand fit in this simplified model. Real budgets also reserve computer memory for the operating system and other uses; shared memory pages must not be counted repeatedly as different physical copies.

Peter J. Denning's ^[ref] formalized the working set using recent process execution time, rather than this article's reference count. Its central insight connected memory demand to process scheduling: deciding which operating-system processes run also decides which working sets need room. Temporal locality makes recent use informative, without making it a promise about the future. The beautiful distinction is between a large possible address space and the smaller footprint of the current activity.