# STM implementation model

## Abstract

This paper present an implementation of a Software Transactional Memory in the context of a high level virtual machine, which is very different than a typical STM implementation that assumes more C-level semantics from the user. This gives us the opportunity to implement an STM in the context of having an efficient Garbage Collector, read, write and pointer comparison barriers and references that can be moving pointers. XXX write down what it gives in exchange

## Overview

The approach we're presenting differs much from the classic STM one. In our approach, the transactions are generally unbound - which means they can be arbitrarily large without much performance hit.

XXX how exactly this removes the need for nested transactions?

XXX

## Overview of the object model

In this model there are two kinds of objects. As an object gets allocated, it's created as a local object that's only visible to the current thread. Local object operations are completely like STM-less operations, the fields are just modified.

Objects are either global (visible to everybody, and read-only), or they are local (visible only to the current thread).

Objects start by being local: when a thread allocates new objects, they are not visible to other threads until a commit occurs. When the commit occurs, the surviving local objects become global.

Once an object is global, its content never changes any more: only parts of the object header can be updated by the STM mechanisms.

If a following transaction modifies a global object, the changes are done in a local copy. If this transaction successfully commits, the original global object is not changed --- it is really immutable. But the copy becomes global, and the old global object's header is updated with a pointer to the new global object. We thus make a chained list of global revisions.

It is the job of the GC to collect the older revisions when they are not referenced any more by any thread.

### CPUs model

For our purposes the following simplified model is enough (x86 only): every CPU's load instructions get the current value from the main memory (the cache is transparent). However, a CPU's store instructions might be delayed and only show up later in main memory. The delayed stores are always flushed to main memory in program order.

Of course if the same CPU loads a value it just stored, it will see the value as modified (self-consistency); but other CPUs might temporarily see the old value.

The MFENCE instruction waits until all delayed stores from this CPU have been flushed. (A CPU has no built-in way to wait until other CPUs' stores are flushed.)

The LOCK CMPXCHG instruction does a MFENCE followed by an atomic compare-and-exchange operation.

## The STM implementation

Every object has a header with these fields:

• h_global (boolean)
• h_possibly_outdated (boolean)
• h_written (boolean)
• h_local_copy (boolean)
• h_revision (unsigned integer)

The h_revision is an unsigned "revision number" that can also alternatively contain a pointer. The other fields are flags. (In practice they are just bits inside the GC h_tid field.)

• h_global means that the object is a global object.
• h_possibly_outdated is used as an optimization: it means that the object is possibly outdated. It is False for all local objects. It is also False if the object is a global object, is the most recent of its chained list of revisions, and is known to have no modified local version in any transaction.
• h_written is set on local objects that have been written to. It is false on global objects.
• h_local_copy is set on local objects that are a copy of a global object. It is undefined on global objects.
• h_revision on local objects points to the global object that they come from, if any (i.e. if h_local_copy); otherwise it is undefined (and can be used for other purposes, e.g. by the GC).
• h_revision on global objects depends on whether the object is the head of the chained list of revisions or not. If it is, then h_revision contains the "timestamp" of the revision at which this version of the object was committed. This is an odd number. For non-head revisions, h_revision is a pointer to a more recent revision. A pointer is always an even number.

### Transaction details

Every CPU is either running one transaction, or is busy trying to commit the transaction it has so far. The following data is transaction-local:

• start_time
• is_inevitable
• global_to_local
• my_lock

The start_time is the "time" at which the transaction started. All reads and writes done so far in the transaction appear consistent with the state at time start_time. The global "time" is a single global number that is atomically incremented whenever a transaction commits.

is_inevitable is a flag described later.

global_to_local is a dictionary-like mapping of global objects to their corresponding local objects.

list_of_read_objects is a set of all global objects read from, in the revision that was used for reading. It is actually implemented as a list, but the order or repetition of elements in the list is irrelevant. This list includes all objects that are keys in global_to_local: we don't have the notion of "write-only object".

recent_reads_cache is a fixed-size cache that remembers recent additions to the preceeding list, in order to avoid inserting too much repeated entries into the list, as well as keep lightweight statistics.

my_lock is a constant in each thread: it is a very large (>= LOCKED) odd number that identifies the thread in which the transaction runs.

The read/write barriers are designed with the following goals in mind:

• In the source code (graphs from RPython), variables containing pointers can be annotated as beloning to one of 6 categories:
• P is a pointer to any object.
• G is a pointer to a global object.
• R is a pointer to an object that was checked for being read-ready: reading its fields is ok.
• O is an old pointer that used to be read-ready, but in which we may have written to in the meantime
• L is a pointer to a local object. We can always read from but not necessarily write to local objects.
• W is a pointer to a writable local object.
• The goal is to insert calls to the following write barriers so that we only ever read from objects in the R, L or W categories, and only ever write to objects in the W category.
• Global objects are immutable, and so can only contain pointers to further global objects.
• The read barriers themselves need to ensure that list_of_read_objects contains exactly the set of global objects that have been read from. These objects must all be of the most recent revision that is not more recent than start_time. If an object has got a revision more recent than start_time, then the current transaction is in conflict. The transaction is aborted as soon as this case is detected.
• The write barriers make sure that all modified objects are local and the h_written flag is set.
• All barriers ensure that global_to_local satisfies the following property for any local object L: either L was created by this transaction (L->h_revision is undefined) or else satisfies global_to_local[L->h_revision] == L.

W = Allocate(size) allocates a local object:

def Allocate(size):
W = malloc(size)
W->h_global = False
W->h_possibly_outdated = False
W->h_written = True
W->h_local_copy = False
#W->h_revision can be left uninitialized
return W


R = LatestGlobalRevision(G) takes a pointer G to a global object, and if necessary follows the chain of newer revisions, until it reaches the most recent revision R. Then it checks the revision number of R to see that it was not created after start_time. Pseudo-code:

def LatestGlobalRevision(G, ...):
R = G
while not (v := R->h_revision) & 1:# "is a pointer", i.e.
R = v                          #   "has a more recent revision"
if v > start_time:                 # object too recent?
if V >= LOCKED:                # object actually locked?
goto retry                 # spin-loop to start of func
ValidateNow()                  # try to move start_time forward
goto retry                     # restart searching from R
PossiblyUpdateChain(G, R, ...)     # see below
return R


R = DirectReadBarrier(P) is the first version of the read barrier. It takes a random pointer P and returns a possibly different pointer R out of which we can read from the object. The result R remains valid for read access until either the current transaction ends, or until a write into the same object is done. Pseudo-code:

def DirectReadBarrier(P, ...):
if not P->h_global:                    # fast-path
return P
if not P->h_possibly_outdated:
R = P
else:
R = LatestGlobalRevision(P, ...)
if R->h_possibly_outdated and R in global_to_local:
L = ReadGlobalToLocal(R, ...)  # see below
return L
return R


A simple optimization is possible. Assume that O is a pointer returned by a previous call to DirectReadBarrier and the current transaction is still running, but we could have written to O in the meantime. Then we need to repeat only part of the logic, because we don't need AddInReadSet again. It gives this:

def RepeatReadBarrier(O, ...):
if not O->h_possibly_outdated:       # fast-path
return O
# LatestGlobalRevision(O) would either return O or abort
# the whole transaction, so omitting it is not wrong
if O in global_to_local:
L = ReadGlobalToLocal(O, ...)    # see below
return L
R = O
return R


L = Localize(R) is an operation that takes a read-ready pointer to a global object and returns a corresponding pointer to a local object:

def Localize(R):
assert R->h_global
if R in global_to_local:
return global_to_local[R]
L = malloc(sizeof R)
L->h_global = False
L->h_possibly_outdated = False
L->h_written = False
L->h_local_copy = True
L->h_revision = R          # back-reference to the original
L->objectbody... = R->objectbody...
global_to_local[R] = L
return L

if R->h_global:
L = Localize(R)
else:
L = R
return L


W = WriteBarrier(P) and W = WriteBarrierFromReadReady(R) are two versions of the write barrier:

def WriteBarrier(P):
if P->h_written:          # fast-path
return P
if not P->h_global:
W = P
R = W->h_revision
else:
if P->h_possibly_outdated:
R = LatestGlobalRevision(P)
else:
R = P
W = Localize(R)
W->h_written = True
R->h_possibly_outdated = True
return W

if R->h_written:          # fast-path
return R
if not R->h_global:
W = R
R = W->h_revision
else:
W = Localize(R)
W->h_written = True
R->h_possibly_outdated = True
return W


### Auto-localization of some objects

The "fast-path" markers above are quick checks that are supposed to be inlined in the caller, so that we only have to pay for a full call to a barrier implementation when the fast-path fails.

However, even the fast-path of DirectReadBarrier fails repeatedly when the DirectReadBarrier is invoked repeatedly on the same set of global objects. This occurs in example of code that repeatedly traverses the same data structure, visiting the same objects over and over again.

If the objects that make up the data structure were local, then we would completely avoid triggering the read barrier's implementation. So occasionally, it is better to localize global objects even when they are only read from.

The idea of localization is to break the strict rule that, as long as we don't write anything, we can only find more global objects starting from a global object. This is relaxed here by occasionally making a local copy even though we don't write to the object.

This is done by tweaking AddInReadSet, whose main purpose is to record the read object in a set (actually a list):

def AddInReadSet(R):
# the cache is fixed-size, so the line above
# possibly evinces another older entry
return R
else:
count += 1
if count < THRESHOLD:
return R
else:
L = Localize(R)
return L


Note that the localized objects are just copies of the global objects. So all the pointers they normally contain are pointers to further global objects. If we have a data structure involving a number of objects, when traversing it we are going to fetch global pointers out of localized objects, and we still need read barriers to go from the global objects to the next local objects.

To get the most out of the optimization above, we also need to "fix" local objects to change their pointers to go directly to further local objects.

So L = ReadGlobalToLocal(R, R_Container, FieldName) is called with optionally R_Container and FieldName referencing some container's field out of which R was read:

def ReadGlobalToLocal(R, R_Container, FieldName):
L = global_to_local[R]
if not R_Container->h_global:
L_Container = R_Container
L_Container->FieldName = L     # fix in-place
return L


Finally, a similar optimization can be applied in LatestGlobalRevision. After it follows the chain of global revisions, it can "compress" that chain in case it contained several hops, and also update the original container's field to point directly to the latest version:

def PossiblyUpdateChain(G, R, R_Container, FieldName):
if R != G and Rarely():
# compress the chain one step (cannot compress the whole chain!)
G->h_revision = R
# update the original field
R_Container->FieldName = R


This last line is a violation of the rule that global objects are immutable. It still works because it is only an optimization that will avoid some chain-walking in the future. If two threads conflict in updating the same field to possibly different values, it is undefined what exactly occurs: other CPUs can see either the original or any of the modified values. It works because the original and each modified value are all interchangeable as far as correctness goes.

However, note that if the chain is longer than one item, we cannot fix the whole chain -- we can only fix the first item. The issue is that we cannot at this point reliably walk the chain again until we reach R, precisely because another thread might be fixing the same chain in such a way that R is then skipped.

Rarely uses a thread-local counter to return True only rarely. We do the above update only rarely, rather than always, although it would naively seem that doing the update always is a good idea. The problem is that it generates a lot of write traffic to global data that is potentially shared between CPUs. We will need more measurements, but it seems that doing it too often causes CPUs to stall. It is probable that updates done by one CPU are sent to other CPUs at high cost, even though these updates are not so important in this particular case (i.e. the program would work fine if the other CPUs didn't see such updates at all and instead repeated the same update logic locally).

### Validation

ValidateDuringTransaction is called during a transaction just after start_time has been updated. It makes sure that none of the read objects have been modified since start_time. If one of these objects is modified by another commit in parallel, then we want this transaction to eventually fail. More precisely, it will fail the next time ValidateDuringTransaction is called.

Note a subtle point: if an object is currently locked, we have to wait until it gets unlocked, because it might turn out to point to a more recent version that is still older than the current global time.

Here is ValidateDuringTransaction:

def ValidateDuringTransaction(during_commit):
v = R->h_revision
if not (v & 1):             # "is a pointer", i.e.
return False            #   "has a more recent revision"
if v >= LOCKED:             # locked
if not during_commit:
assert v != my_lock # we don't hold any lock
spin loop retry     # jump back to the "v = ..." line
else:
if v != my_lock:    # not locked by me: conflict
return False
return True

def ValidateNow():
start_time = GetGlobalCurTime()      # copy from the global time
if not ValidateDuringTransaction(0): # do validation
AbortTransaction()               # if it fails, abort


Checking for my_lock is only useful when ValidateDuringTransaction is called during commit, which is when we actually hold locks. In that case, detecting other already-locked objects causes a conflict. Note that we should in general never spin-loop during commit; other threads might be blocked by the fact that we own locks already, causing a deadlock.

### Local garbage collection

Before we can commit, we need the system to perform a "local garbage collection" step. The problem is that recent objects (obtained with Allocate during the transaction) must originally have the h_global flag set to False, but this must be changed to True before the commit is complete. While we could make a chained list of all such objects and change all their h_global flags now, such an operation is wasteful: at least in PyPy, the vast majority of such objects are already garbage.

Instead, we describe here the garbage collection mechanism used in PyPy (with its STM-specific tweaks). All newly allocated objects during a transaction are obtained from a thread-specific "nursery". The nursery is empty when the transaction starts. If the nursery fills up during the execution of the transaction, a "minor collection" cycle moves the surviving objects outside. All these objects, both from the nursery and those moved outside, have the h_global flag set to False.

At the end of the transaction, we perform a "local collection" cycle. The main goal is to make surviving objects non-movable --- they cannot live in any thread-local nursery as soon as they are visible from other threads. If they did, we could no longer clear the content of the nursery when it fills up later.

The secondary goal of the local collection is to change the header flags of all surviving objects: their h_global is set to True. As an optimization, during this step, all pointers that reference a local but not written to object are changed to point directly to the original global object.

Actual committing occurs after the local collection cycle is complete, when all reachable objects are h_global.

Hand-wavy pseudo-code:

def FinishTransaction():
FindRootsForLocalCollect()
PerformLocalCollect()
CommitTransaction()          # see below

def FindRootsForLocalCollect():
for (R, L) in global_to_local:
if not L->h_written:     # non-written local objs are dropped
L->h_global = True   # (becoming global and outdated -> R)
L->h_possibly_outdated = True
continue

def PerformLocalCollect():
collect from the roots...
for all reached local object,
change h_global False->True
change h_written True->False
if not h_local_copy:
h_revision = 1


Note that non-written local objects are just shadow copies of existing global objects. For the sequel we just replace them with the original global objects again. This is done by tweaking the local objects' header.

Note also that h_revision is free to be (ab)used on newly allocated objects (the GC of PyPy does this), but it should be set to 1 just before calling CommitTransaction.

### Committing

Committing is a four-steps process:

1. We first take all global objects with a local copy that has been written to, and mark them "locked" by putting in their h_revision field a special value that will cause parallel CPUs to spin loop in LatestGlobalRevision.

1. We atomically increase the global time (with LOCK CMPXCHG).

3. We check again that all read objects are still up-to-date, i.e. have not been replaced by a revision more recent than start_time. (This is the last chance to abort a conflicting transaction; if we do, we have to remember to release the locks.)

4. Finally, we unlock the global objects by overriding their h_revision. We put there now a pointer to the corresponding previously-local object, and the previously-local object's header is fixed so that it plays from now on the role of the global head of the chained list.

In pseudo-code:

def CommitTransaction():
# (see below for the full version with inevitable transactions)
AcquireLocks()
cur_time = global_cur_time
while not CMPXCHG(&global_cur_time, cur_time, cur_time + 2):
cur_time = global_cur_time    # try again
if cur_time != start_time:
if not ValidateDuringTransaction(1): # only call it if needed
AbortTransaction()               # last abort point


Note the general style of usage of CMPXCHG: we first read normally the current version of some data (here global_cur_time), and then do the expensive CMPXCHG operation. It checks atomically if the value of the data is still equal to the old value; if yes, it replaces it with a new specified value and returns True; otherwise, it simply returns False. In the latter case we just loop again. (A simple case like this could also be done with XADD, with a locked increment-by-two.)

Here is AcquireLocks, locking the global objects. Note that "locking" here only means writing a value >= LOCKED in the h_revision field; it does not involve OS-specific thread locks:

def AcquireLocks():
for (R, L, 0) in gcroots SORTED BY R:
v = R->h_revision
if not (v & 1):         # "is a pointer", i.e.
AbortTransaction()  #   "has a more recent revision"
if v >= LOCKED:         # already locked by someone else
spin loop retry     # jump back to the "v = ..." line
if not CMPXCHG(&R->h_revision, v, my_lock):
spin loop retry     # jump back to the "v = ..." line
save v into the third item in gcroots, replacing the 0


We use CMPXCHG to store the lock. This is required, because we must not conflict with another CPU that would try to write its own lock in the same field --- in that case, only one CPU can succeed.

Acquiring multiple locks comes with the question of how to avoid deadlocks. In this case, it is prevented by ordering the lock acquisitions in the numeric order of the R pointers. This should be enough to prevent deadlocks even if two threads have several objects in common in their gcroots.

The lock's value my_lock is, precisely, a very large odd number, at least LOCKED (which should be some value like 0xFFFF0000). Such a value causes LatestGlobalRevision to spin loop until the lock is released (i.e. another value is written in h_revision).

After this, CommitTransaction increases the global time and then calls ValidateDuringTransaction defined above. It may still abort. In case AbortTransaction is called, it must release the locks. This is done by writing back the original timestamps in the h_revision fields:

def CancelLocks():
for (R, L, v) in gcroots:
if v != 0:
R->h_revision = v
reset the entry in gcroots to v=0

def AbortTransaction():
CancelLocks()
# call longjmp(), which is the function from C
# going back to the transaction start
longjmp()


Finally, in case of a successful commit, UpdateChainHeads also releases the locks --- but it does so by writing in h_revision a pointer to the previously-local object, thus increasing the length of the chained list by one:

def UpdateChainHeads(cur_time):
new_revision = cur_time + 1     # make an odd number
for (R, L, v) in gcroots:
L->h_revision = new_revision
smp_wmb()
R->h_revision = L


smp_wmb is a "write memory barrier": it means "make sure the previous writes are sent to the main memory before the succeeding writes". On x86 it is just a "compiler fence", preventing the compiler from doing optimizations that would move the assignment to R->h_revision earlier. On non-x86 CPUs, it is actually a real CPU instruction, needed because the CPU doesn't normally send to main memory the writes in the original program order. (In that situation, it could be more efficiently done by splitting the loop in two: first update all local objects, then only do one smp_wmb, and then update all the R->h_revision fields.)

Note that the Linux documentation pushes forward the need to pair smp_wmb with either smp_read_barrier_depends or smp_rmb. In our case we would need an smp_read_barrier_depends in LatestGlobalRevision, in the loop. It was omitted here because this is always a no-op (i.e. the CPUs always provide this effect for us), not only on x86 but on all modern CPUs.

### Inevitable transactions

A transaction is "inevitable" when it cannot abort any more. It occurs typically when the transaction tries to do I/O or a similar effect that we cannot roll back. Such effects are O.K., but they mean that we have to guarantee the transaction's eventual successful commit.

The main restriction is that there can be only one inevitable transaction at a time. Right now the model doesn't allow any other transaction to start or commit when there is an inevitable transaction; this restriction could be lifted with additional work.

For now, the hint that the system has currently got an inevitable transaction running is given by the value stored in global_cur_time: the largest positive number (equal to the INEVITABLE constant).

BecomeInevitable is called from the middle of a transaction to (attempt to) make the current transaction inevitable:

def BecomeInevitable():
inevitable_mutex.acquire()
cur_time = global_cur_time
while not CMPXCHG(&global_cur_time, cur_time, INEVITABLE):
cur_time = global_cur_time    # try again
if start_time != cur_time:
start_time = cur_time
if not ValidateDuringTransaction(0):
global_cur_time = cur_time     # must restore the value
inevitable_mutex.release()
AbortTransaction()
is_inevitable = True


We use a normal OS mutex to allow other threads to really sleep instead of spin-looping until the inevitable transaction finishes. So the function GetGlobalCurTime is defined to return global_cur_time after waiting for other inevitable transaction to finish:

def GetGlobalCurTime():
assert not is_inevitable    # must not be myself inevitable
t = global_cur_time
if t == INEVITABLE:         # there is another inevitable tr.?
inevitable_mutex.acquire()   # wait
inevitable_mutex.release()
return GetGlobalCurTime()    # retry
return t


Then we extend CommitTransaction for inevitable support:

def CommitTransaction():
AcquireLocks()
if is_inevitable:
cur_time = start_time
if not CMPXCHG(&global_cur_time, INEVITABLE, cur_time + 2):
unreachable: no other thread changed global_cur_time
inevitable_mutex.release()
else:
cur_time = GetGlobalCurTimeInCommit()
while not CMPXCHG(&global_cur_time, cur_time, cur_time + 2):
cur_time = GetGlobalCurTimeInCommit()  # try again
if cur_time != start_time:
if not ValidateDuringTransaction(1): # only call it if needed
AbortTransaction()               # last abort point

def GetGlobalCurTimeInCommit():
t = global_cur_time
if t == INEVITABLE:
CancelLocks()
inevitable_mutex.acquire()   # wait until released
inevitable_mutex.release()
AcquireLocks()
return GetGlobalCurTimeInCommit()
return t


## Barrier placement in the source code

### Overview

Placing the read/write barriers in the source code is not necessarily straightforward, because there are a lot of object states to choose from. The barriers described above are just the most common cases.

We classify here the object categories more precisely. A pointer to an object in the category R might actually point to one that is in the more precise category L or W. Conversely, a pointer to an object in the category L is also always in the categories R or O. This can be seen more generally in the implication relationships:

W => L => R => O => P       G => P    (I)


A letter X is called more general than a letter Y if Y => X, and more precise than a letter Y if X => Y.

Barriers are used to make an object's category more precise. Here are all 12 interesting conversions, with the five functions from the section Read/write barriers design (abbreviated as DRB, RRB, LRR, WrB and WFR) as well as seven more potential conversions (written *) that could be implemented efficiently with slight variations:

From
To P G O R L W
R DRB * RRB
L * * * LRR
W WrB * * WFR *

In the sequel we will refer to each of the 12 variations as X2Y for X in P, G, O, R, L and Y in R, L, W.

### Constraints

The source code's pointer variables are each assigned one letter from P, G, O, R, L, W such that:

• A variable is only passed into another variable with either the same or a more general letter. This holds for intra- as well as inter-procedural definitions of "being passed" (i.e. also for arguments and return value).
• Read/write barriers can be inserted at any point, returning a variable of a more precise letter.
• Any read must be done on an object in category R, L, W. Any write must be done on an object in category W. Moreover an object must only be in category W if we can prove that a write necessarily occurs on the object.
• The L2W barrier is very cheap. It is also the only barrier which doesn't need to return a potentially different pointer. However, converting objects to the L category in first place (rather than R) has a cost. It should be done only for the objects on which we are likely to perform a write.
• An object in the R category falls back automatically to the O category if we perform an operation (like a call to an unrelated function) that might potentially cause it to be written to.
• If we do a call that might cause the current transaction to end and the next one to start, then all live variables fall back to the P category.
• The G category is only used by prebuilt constants. In all other cases we don't know that a pointer is definitely not a local pointer. The NULL constant is in all categories; G and L have only NULL in common.
• In general, it is useful to minimize the number of executed barriers, and have the cheapest barriers possible. If, for example, we have a control flow graph with two paths that reach (unconditionally) the same write location, but on one path the object is a R (because we just read something out of it) and on the other path the object is a G (because it is a global on which we did not perform any read), then we should insert the R2W barrier at the end of the first path and the G2W barrier at the end of the second path, rather than the P2W barrier only once after the control flow merges.

Pseudo-code for some of the remaining barriers:

def G2R(G):
assert G->h_global
return P2R(G)        # the fast-path never works

def G2W(G):
assert G->h_global
assert not G->h_written
if G->h_possibly_outdated:
R = LatestGlobalRevision(G)
else:
R = G
W = Localize(R)
W->h_written = True
R->h_possibly_outdated = True
return W

def L2W(L):
if L->h_written:    # fast-path
return L
L->h_written = True
L->h_revision->h_possibly_outdated = True
return L


Pointer equality: a comparison P1 == P2 needs special care, because there are several physical pointers corresponding logically to the same object. If P1 or P2 is the constant NULL then no special treatment is needed. Likewise if P1 and P2 are both known to be local. Otherwise, we need in general the following code (which could be specialized as well if needed):

def PtrEq(P1, P2):
return GlobalizeForComparison(P1) == GlobalizeForComparison(P2)

def GlobalizeForComparison(P):
if P == NULL:
return NULL
elif P->h_global:
return LatestGlobalRevision(P)
elif P->h_local_copy:
return P->h_revision  # return the original global obj
else:
return P

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Tip: Use camelCasing e.g. ProjME to search for ProjectModifiedEvent.java.
Tip: Filter by extension type e.g. /repo .js to search for all .js files in the /repo directory.
Tip: Separate your search with spaces e.g. /ssh pom.xml to search for src/ssh/pom.xml.
Tip: Use ↑ and ↓ arrow keys to navigate and return to view the file.
Tip: You can also navigate files with Ctrl+j (next) and Ctrl+k (previous) and view the file with Ctrl+o.
Tip: You can also navigate files with Alt+j (next) and Alt+k (previous) and view the file with Alt+o.