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1 Memory Management Managing memory hierarchies. 2 Memory Management Ideally programmers want memory that is –large –fast –non volatile –transparent Memory.

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Presentation on theme: "1 Memory Management Managing memory hierarchies. 2 Memory Management Ideally programmers want memory that is –large –fast –non volatile –transparent Memory."— Presentation transcript:

1 1 Memory Management Managing memory hierarchies

2 2 Memory Management Ideally programmers want memory that is –large –fast –non volatile –transparent Memory hierarchy –small amount of fast, expensive memory – cache –some medium-speed, medium price main memory –gigabytes of slow, cheap disk storage Memory manager handles the memory hierarchy

3 3 Basic Memory Management Monoprogramming without Swapping or Paging Three simple ways of organizing memory - an operating system with one user process

4 4 Multiprogramming We usually want more than one program… –Multiprogramming! Flexibility Utilization (CPU vs. I/O) Multi-user environments First try: partition the memory!

5 5 Multiprogramming with Fixed Partitions Fixed memory partitions –separate input queues for each partition –single input queue

6 6 Multiprogramming Sidestep: How much do we actually gain from multiprogramming? 5 80% idle = 100% CPU utilization?

7 7 Modeling Multiprogramming Degree of multiprogramming

8 8 Analysis of Multiprogramming System Performance Arrival and work requirements of 4 jobs CPU utilization for 1 – 4 jobs with 80% I/O wait Sequence of events as jobs arrive and finish –note numbers show amount of CPU time jobs get in each interval

9 9 Where to place a program? Where in the memory should we place our programs? –Memory starts at 0 and ends at 2 32 ! –We might want more than one program! –Programs might have different sizes! –Program size might grow (or shrink)!

10 10 Swapping (1) Memory allocation changes as –processes come into memory –leave memory Solution for relocation needed

11 11 Swapping (2) Allocating space for growing data segment Allocating space for growing stack & data segment

12 12 Memory Management with Bit Maps Part of memory with 5 processes, 3 holes –tick marks show allocation units –shaded regions are free Corresponding bit map Same information as a list

13 13 Memory Management with Linked Lists Four neighbor combinations for the terminating process X

14 14 Issues with memory We want: –A lot of memory (more than RAM) –Transparency (Relocation) –Protection (rouge processes) –Speed (Cache is fast, RAM is OK, disk is slow) (Partial) Solution: Virtual Memory

15 15 Virtual Memory Separates: –Virtual (logical) addresses –Physical addresses Requires a translation at run time –Virtual  Physical –Handled in HW (MMU)

16 16 Virtual Memory Paging The position and function of the MMU

17 17 Paging The relation between virtual addresses and physical memory addres- ses given by page table One page table per process is needed (per thread?) Page table needs to be reloaded at context switch

18 18 Address Translation © Patterson & Hennesy 1998

19 19 Page Tables Internal operation of MMU with 16 4 KB pages

20 20 Hierarchical Page Tables 32 bit address with 2 page table fields Two-level page tables Second-level page tables Top-level page table

21 21 Page Tables Typical page table entry

22 22 Paging Every memory lookup: 1.Find the page in the page table 2.Find the (physical) memory location Now we have two memory accesses (per reference)  Solution: Translation Lookaside Buffer (TLB) (again a cache…)

23 23 TLBs – Translation Lookaside Buffers A TLB to speed up paging

24 24 TLB Handling Memory lookup: Look for page in TLB (fast) –If hit, fine go ahead! –If miss, find it and put it in the TLB: Find the page in the page table (hit) Reload the page from disk (miss)

25 25 Inverted Page Tables Comparison of a traditional page table with an inverted page table

26 26 So where is the cache? © Patterson & Hennesy 1998

27 27 Paging So we know how to find a page table entry If it is not in the page table –Get it from disk What if the physical memory is full? –Throw some page out! –Remember cache replacement policies?

28 28 Page Replacement Algorithms Page fault forces choice –which page must be removed –make room for incoming page(s) Modified page must first be saved –unmodified just overwritten Better not to choose an often used page –will probably need to be brought back in soon

29 29 Optimal Page Replacement Algorithm Replace page needed at the farthest point in future –Optimal but unrealizable Estimate by … –logging page use on previous runs of process –although this is impractical

30 30 Not Recently Used Page Replacement Algorithm Each page has Reference bit, Modified bit –Bits are set by HW when page is referenced, modified –Reference bit is periodically unset (at clock ticks) Pages are classified Class 0: not referenced, not modified Class 1: not referenced, modified Class 2: referenced, not modified Class 3: referenced, modified NRU removes page at random –From lowest numbered non empty class NRU is simple and gives decent performance

31 31 FIFO Page Replacement Algorithm Maintain a linked list of all pages –in the order they came into memory Page at beginning of list replaced Disadvantage –page in memory the longest may be used often

32 32 Second Chance Page Replacement Algorithm Operation of a second chance –pages sorted in FIFO order –Inspect the R bit, give the page a second chance if R=1 –Put the page at the end of the list (like a new page) –Eventually the page might be selected anyway (how?) Example: Page 20, A has R bit set 2 nd Chance moves pages around in the list …

33 33 The Clock Page Replacement Algorithm How does this compare with second chance? Hand points to oldest page

34 34 Locality Locality in: –Time A page recently used, will be used soon again –Space Adjacent pages are likely to be used together

35 35 Least Recently Used (LRU) Locality: pages used recently will be used soon –throw out the page that has been unused longest Must keep a linked list of pages –most recently used at front, least at rear –update this list every memory reference !! Alternative: counter (64 bit) in the page table entry –Update on memory reference –choose page with lowest value counter –periodically zero the counter

36 36 Least Recently Used LRU using a matrix – pages referenced in order 0,1,2,3,2,1,0,3,2,3 1.Set row k to 1 2.Set column k to 0

37 37 Simulating LRU in Software Both solutions (counter, matrix) require extra hardware and they don’t scale very well with large page tables … Approximations of LRU can be done in SW

38 38 Simulating LRU in Software NFU (Not Frequently Used) A counter is associated with each page At each clock interrupt add R to the counter Problems?

39 39 NFU NFU tends to have a long memory! Pages frequently used in pass 1 may still be hot in later passes, even though they are not used! Solution: instead of adding R, shift R from the left (R=1, c=010  c’=101) This algorithm is called aging

40 40 Simulating LRU in Software The aging algorithm simulates LRU in software Note 6 pages for 5 clock ticks, (a) – (e)

41 41 Aging This algorithm works fine, but consider –Granularity Page accesses between ticks? –Memory How long is the memory? Tannenbaum: 8 20ms ticks works ok

42 42 Memory Management Managing hierarchies of memory –Caches –RAM –Disk Paging –Page tables –Page replacement algorithms Virtual Memory


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