Predicting Lifetimes in Dynamically Allocated Memory
Authors: David A. Cohn, Satinder P. Singh
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Predictions oflifetimes of dynamically allocated objects can be used  to improve time and space efficiency  of dynamic memory manage(cid:173) ment in computer programs.  Barrett and Zorn  [1993]  used a simple  lifetime predictor and demonstrated this improvement on a variety  of computer programs.  In  this  paper,  we  use  decision  trees  to  do  lifetime  prediction  on  the  same  programs  and  show  significantly  better  prediction .  Our method also has the advantage that  during  training we  can use  a large number of features  and let the decision  tree  automatically choose  the relevant  subset. 
1 
INTELLIGENT MEMORY ALLOCATION 
Dynamic  memory  allocation  is  used  in  many  computer  applications.  The  appli(cid:173) cation  requests  blocks  of memory from  the  operating  system  or  from  a  memory  manager when  needed  and explicitly frees  them up after use.  Typically, all of these  requests  are handled  in  the same way,  without  any  regard  for  how  or for  how  long  the  requested  block  will  be  used.  Sometimes programmers use  runtime profiles  to  analyze  the  typical  behavior  of their  program  and  write  special  purpose  memory  management  routines  specifically  tuned  to  dominant  classes  of allocation  events.  Machine learning methods offer  the opportunity to automate the process  of tuning  memory management systems. 
In  a  recent  study,  Barrett  and  Zorn  [1993]  used  two  allocators:  a  special  allocator  for  objects  that  are  short-lived,  and  a  default  allocator  for  everything  else.  They  tried a simple prediction method on a number of public-domain , allocation-intensive  programs and  got  mixed results  on  the  lifetime prediction  problem.  Nevertheless,  they showed that for all the cases where they were able to predict well,  their strategy  of assigning objects predicted to be short-lived to the special allocator led to savings 
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in  program running  times.  Their results  imply that  if we  could  predict  well  in  all  cases  we  could  get  similar savings for  all programs.  We  concentrate on  the lifetime  prediction  task  in  this  paper  and  show  that using  axis-parallel  decision  trees  does  indeed lead to significantly better prediction on all the programs studied by Zorn and  Grunwald  and  some others  that we  included.  Another  advantage of our approach  is that we  can  use  a large number of features  about the allocation requests  and  let  the decision  tree decide  on their relevance.  There are  a number of advantages of using lifetime predictions for  intelligent mem(cid:173) ory  management.  It can  improve CPU  usage,  by  using special-purpose  allocators,  e.g., short-lived objects can be allocated  in small spaces by  incrementing a  pointer  and deallocated together when they  are  all dead.  It can decrease  memory fragmen(cid:173) tation, because the short-lived objects do not pollute the address space of long lived  objects.  Finally, it can  improve program locality, and thus program speed,  because  the short-lived objects  are  all allocated in a  small part of the  heap. 
The advantages of prediction must be weighed against the time required to examine  each  request  and  make  that  prediction  about  its  intended  use.  It is  frequently  argued  that,  as  computers  and  memory  become  faster  and  cheaper,  we  need  to  be  less  concerned  about  the  speed  and  efficiency  of machine  learning  algorithms.  When  the  purpose  of the  algorithm  is  to  save  space  and  computation,  however,  these  concerns  are paramount. 
1.1  RELATED  WORK 
Traditionally, memory management has been  relegated  to a single,  general-purpose  allocator.  When performance is  critical, software  developers will frequently  build a  custom memory manager which  they  believe is tuned  to optimize the  performance  of the  program.  Not  only  is  this hand construction  inefficient  in  terms of the pro(cid:173) gramming time required,  this  "optimization" may seriously  degrade the  program's  performance if it does not accurately reflect  the program's use  [Wilson et al., 1995].  Customalloc [Grunwald and  Zorn,  1992]  monitors program runs on  benchmark in(cid:173) puts  to  determine  the  most  commonly  requested  block  sizes.  It then  produces  a  set of memory allocation routines which  are customized to efficiently allocate those  block sizes.  Other memory requests  are still handled by a general purpose allocator. 
Barrett  and Zorn  [1993]  studied lifetime prediction based on benchmark inputs.  At  each allocation request, the call graph (the list of nested  procedure/function calls in  effect  at the time)  and the object  size  was used  to identify  an  allocation  site.  If all  allocations from  a  particular site  were  short-lived  on  the  benchmark  inputs,  their  algorithm predicted that future allocations would also be short-lived.  Their method  produced  mixed  results  at  lifetime prediction,  but  demonstrated  the  savings  that  such  predictions could bring. 
In  this  paper,  we  discuss  an  approach  to  lifetime  prediction  which  uses  learned  decision  trees.  In  the  next  section,  we  first  discuss  the  identification  of relevant  state features  by  a  decision  tree.  Section  3 discusses  in greater  detail  the problem  of lifetime  prediction.  Section  4  describes  the  empirical  results  of applying  this  approach  to several  benchmark programs, and Section  5 discusses  the implications  of these  results  and directions for  future  work. 
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2  FEATURE  SELECTION WITH  A  DECISION TREE 
Barrett and Zorn 's approach captures state information in the form of the program's  call  graph  at  the  time of an  allocation  request,  which  is  recorded  to  a  fixed  pre(cid:173) determined depth.  This graph,  plus the request  size, specifies  an  allocation  "site";  statistics are gathered separately for each site.  A drawback of this approach is that  it forces  a  division for  each  distinct  call  graph,  preventing  generalization across  ir(cid:173) relevant  features.  Computationally, it  requires  maintaining an  explicit  call  graph  (information that  the  program  would  not  normally provide),  as  well  as  storing  a  potentially large  table of call  sites from  which  to make predictions.  It also  ignores  other potentially useful  information, such as the parameters of the functions on the  call stack,  and the contents of heap  memory and the program registers  at the time  of the request. 
Ideally, we  would  like  to examine as  much  of the program state  as  possible  at the  time of each  allocation request,  and  automatically extract  those  pieces  of informa(cid:173) tion that best  allow  predicting how  the requested  block  will be used.  Decision  tree  algorithms are  useful  for  this sort  of task.  A  decision  tree  divides  inputs on  basis  of how  each  input feature  improves  "purity"  of the  tree's  leaves.  Inputs  that  are  statistically  irrelevant  for  prediction  are  not  used  in  any  splits;  the  tree's final  set  of decisions examine only input features  that improve its predictive performance. 
Regardless of the parsimony of the final tree however,  training a tree with the entire  program state as a feature vector is  computationally infeasible. In our experiments,  detailed  below,  we  arbitrarily  used  the  top  20  words  on  the  stack,  along  with  the  request  size,  as  an approximate indicator of program state.  On the target  machine  (a Sparcstation) , we found that including program registers in the feature set made  no significant difference,  and so  dropped  them from  consideration for  efficiency. 
3  LIFETIME  PREDICTION 
The characteristic of memory requests that we  would like to predict  is  the lifetime  of the  block - how  long it  will  be  before  the  requested  memory is  returned  to the  central  pool.  Accurate  lifetime  prediction  lets  one  segregate  memory  into  short(cid:173) term, long-term and permanent storage.  To this end,  we  have  used  a  decision  tree  learning  algorithm to  derive  rules  that distinguish  "short-lived"  and  "permanent"  allocations from the general  pool of allocation requests. 
For short-lived blocks, one  can  create a  very  simple and efficient  allocation scheme  [Barrett  and  Zorn,  1993].  For  "permanent"  blocks,  allocation  is  also  simple  and  cheap, because  the  allocator  does  not  need  to  compute and  store  any of the  infor(cid:173) mation that would normally be required  to keep  track of the block and return it to  the pool when freed . 
One  complication is  that of unequal loss for  different  types of incorrect  predictions.  An appropriately routed memory request may save dozens of instruction cycles,  but  an  inappropriately  routed  one  may  cost  hundreds.  The  cost  in  terms  of memory  may  also  be  unequal:  a  short-lived  block  that  is  incorrectly  predicted  to  be  "per(cid:173) manent"  will  permanently tie up  the space occupied  by  the block  (if it is  allocated  via a  method  that  can  not  be  freed).  A  "permanent"  block,  however,  that  is  in(cid:173) correctly  predicted  to  be  short-lived  may  pollute  the  allocator's short-term space  by  preventing a  large segment of otherwise free  memory from  being reclaimed  (see  Barrett  and  Zorn  for  examples). 
These  risks  translate  into  a  time-space  tradeoff that  depends  on  the  properties  of 
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the specific  allocators used  and the space limitations of the target machine.  For our  experiments,  we  arbitrarily defined  false  positives and false  negatives to have equal  loss,  except  where  noted  otherwise.  Other  cases  may  be  handled  by  reweighting  the  splitting  criterion,  or  by  rebalancing  the  training  inputs  (as  described  in  the  following section).