The code for Orion 2.0 makes use of printf() at several places where there as
an error in configuration of the model. These have been replaced with fatal().
This way things that don't care about work count options and/or aren't called
by something that has those command line options set up doesn't have to build
a fake object to carry in inert values.
By stalling and waiting the mandatory queue instead of recycling it, one can
ensure that no incoming messages are starved when the mandatory queue puts
signficant of pressure on the L1 cache controller (i.e. the ruby memtester).
--HG--
rename : src/mem/slicc/ast/WakeUpDependentsStatementAST.py => src/mem/slicc/ast/WakeUpAllDependentsStatementAST.py
The packet now identifies whether static or dynamic data has been allocated and
is used by Ruby to determine whehter to copy the data pointer into the ruby
request. Subsequently, Ruby can be told not to update phys memory when
receiving packets.
Move page table walker state to its own object type, and make the
walker instantiate state for each outstanding walk. By storing the
states in a queue, the walker is able to handle multiple outstanding
timing requests. Note that functional walks use separate state
elements.
In sendSplitData, keep a pointer to the senderState that may be updated after
the call to handle*Packet. This way, if the receiver updates the packet
senderState, it can still be accessed in sendSplitData.
Double packet delete problem is due to an interrupt device deleting a packet that the SimpleTimingPort also deletes. Since MessagePort descends from SimpleTimingPort, simply reimplement the failing code from SimpleTimingPort: recvTiming.
Separate data VCs and ctrl VCs in garnet, as ctrl VCs have 1 buffer per VC,
while data VCs have > 1 buffers per VC. This is for correct power estimations.
Maintain all information about an instruction's fault in the DynInst object rather
than any cpu-request object. Also, if there is a fault during the execution stage
then just save the fault inside the instruction and trap once the instruction
tries to graduate
Give fetch unit it's own parameterizable fetch buffer to read from. Very inefficient
(architecturally and in simulation) to continually fetch at the granularity of the
wordsize. As expected, the number of fetch memory requests drops dramatically
instead of having one cache-unit class be responsible for both data and code
accesses, separate code that is just for fetch in it's own derived class off the
original base class. This makes the code easier to manage as well as handle
future cases of special fetch handling
allow the user to specify how many instructions a pipeline stage can process
on any given cycle (stageWidth...i.e.bandwidth) by setting the parameter through
the python interface rather than compile the code after changing the *.cc file.
(we always had the parameter there, but still used the static 'ThePipeline::StageWidth'
instead)
-
Since StageWidth is now dynamically defined, change the interstage communication
structure to use a vector and get rid of array and array handling index (toNextStageIndex)
since we can just make calls to the list for the same information
use skidbuffer as only location for instructions between stages. before,
we had the insts queue from the prior stage and the skidbuffer for the
current stage, but that gets confusing and this consolidation helps
when handling squash cases
manage insertion and deletion like a queue but will need
access to internal elements for future changes
Currently, skidbuffer manages any instruction that was
in a stage but could not complete processing, however
we will want to manage all blocked instructions (from prev stage
and from cur. stage) in just one buffer.
