integrating recent changes from fermi-test into fermi

(i'll use "fermi" for more disruptive changes to the pipeline model such
as updating the MSHRs and getting rid of the warp tracker, ripping out DWF, etc...)

[git-p4: depot-paths = "//depot/gpgpu_sim_research/fermi/distribution/": change = 7805]

1 files changed, 0 insertions, 144 deletions

diff --git a/benchmarks/CUDA/DG/3rdParty/ParMetis-3.1/CHANGES b/benchmarks/CUDA/DG/3rdParty/ParMetis-3.1/CHANGES
deleted file mode 100644
index 1b1e6d0..0000000
--- a/benchmarks/CUDA/DG/3rdParty/ParMetis-3.1/CHANGES
+++ /dev/null
@@ -1,144 +0,0 @@
-
-Changes in version 3.1
-- The mesh partitioning and dual creation routines have changed to support mixed
-  element meshes.
-
-- The parmetis.h header file has been restructured and is now C++ friendly.
-
-- Fortran bindings/renamings for various routines have been added.
-
-- A number of bugs have been fixed.
-  - tpwgts are now respected for small graphs.
-  - fixed various divide by zero errors.
-  - removed dependency on the old drand48() routines.
-  - fixed some memory leaks.
-
-
-
-Changes in version 3.0
-
-- The names and calling sequence of all the routines have changed due to expanded
-  functionality that has been provided in this release. However, the 2.0 API calls 
-  have been mapped to the new routines. However, the expanded functionality provided 
-  with this release is only available by using the new calling sequences.
-
-- The four adaptive repartitioning routines: 
-    ParMETIS_RepartLDiffusion, 
-    ParMETIS_RepartGDiffusion,
-    ParMETIS_RepartRemap, and
-    ParMETIS_RepartMLRemap,
-  have been replaced by a single routine called ParMETIS_V3_AdpativeRepart that 
-  implements a unified repartitioning algorithm which combines the best features 
-  of the previous routines.
-
-- Multiple vertex weights/balance constraints are supported for most of the
-  routines. This allows ParMETIS to be used to partition graphs for multi-phase
-  and multi-physics simulations.
-
-- In order to optimize partitionings for specific heterogeneous computing 
-  architectures, it is now possible to specify the target sub-domain weights 
-  for each of the sub-domains and for each balance constraint. This feature, 
-  for example, allows the user to compute a partitioning in which one of the 
-  sub-domains is twice the size of all of the others.
-
-- The number of sub-domains has been de-coupled from the number of processors
-  in both the static and the adaptive partitioning schemes. Hence, it is now
-  possible to use the parallel partitioning and repartitioning algorithms
-  to compute a k-way partitioning independent of the number of processors
-  that are used. Note that Version 2.0 provided this functionality for the 
-  static partitioning schemes only.
-
-- Routines are provided for both directly partitioning a finite element mesh, 
-  and for constructing the dual graph of a mesh in parallel.
-
-
-
-Changes in version 2.0
-
-- Changed the names and calling sequences of all the routines to make it 
-  easier to use ParMETIS with Fortran. 
-
-- Improved the performance of the diffusive adaptive repartitioning 
-  algorithms.
-
-- Added a new set of adaptive repartitioning routines that are based on the
-  remapping paradigm. These routines are called ParMETIS_RepartRemap and
-  ParMETIS_RepartMLRemap
-
-- The number of partitions has been de-coupled from the number of processors.
-  You can now use the parallel partitioning algorithms to compute a k-way
-  partitioning independent of the number of processors that you use.
-
-- The partitioning and ordering algorithms in ParMETIS now utilize various
-  portions of the serial METIS library. As a result of this, the quality
-  of the produced partitionings and orderings have been improved. 
-  Remember to link your code with both libmetis.a and libparmetis.a
-
-
-Changes in version 1.0
-
-- Added partitioning routines that take advantage of coordinate information.
-  These routines are based on space-filling curves and they are used to 
-  quickly compute a initial distribution for PARKMETIS.
-  A total of three routines have been added called PARGKMETIS, PARGRMETIS,
-  and PARGMETIS
-
-- Added a fill-reducing ordering routine that is based on multilevel nested 
-  dissection. This is similar to the ordering routine in the serial Metis 
-  with the difference that is directly computes and refines vertex 
-  separators. The new routine is called PAROMETIS and returns the new ordering
-  of the local nodes plus a vector describing the sizes of the various
-  separators that form the elimination tree.  
-
-- Changed the calling sequence again! I found it awkward to require that
-  communicators and other scalar quantities being passed by reference.
-
-- Fixed a number of memory leaks.
-
-
-
-Changes in version 0.3
-
-- Incorporated parallel multilevel diffusion algorithms for repartitioning
-  adaptively refined meshes. Two routines have been added for this purpose:
-  PARUAMETIS that performs undirected multilevel diffusion
-  PARDAMETIS that performs directed multilevel diffusion
-
-- Changed the names and calling sequences of the parallel partitioning
-  and refinement algorithms. Now they are called PARKMETIS for the
-  k-way partitioning and PARRMETIS for the k-way refinement.
-  Also the calling sequence has been changed slightly to make ParMETIS
-  Fortran callable.
-
-- Added an additional option for selecting the algorithm for initial
-  partitioning at the coarsest graph. Now you have the choice of selecting
-  either a serial or a parallel algorithm. The parallel initial partitioning
-  speeds up the algorithm especially for large number of processors.
-  NOTE that the parallel initial partitioning works only for partitions that
-  are power of two. If you want partitions that are not power of two you must
-  use the old serial initial partitioning option.
-
-- Fixed some bugs in the initial partitioning code.
-
-- Made parallel k-way refinement more robust by randomly ordering the
-  processors at each phase
-
-
-Changes in version 0.2
-
-- A complete reworking of the primary algorithms. The performance
-  of the code has improved considerably. Over 30% on 128 processor
-  Cray T3D. Improvement should be higher on machines with high
-  latencies.
-
-  Here are some performance numbers on T3D using Cray's MPI
-  for 2 graphs, mdual (0.25M vertices) and mdual2 (1.0M vertices)
-
-	       16PEs 	32PEs	64PEs	128PEs
-  mdual		4.07	2.97	2.82	
-  mdual2       15.02	8.89	6.12	5.75
-
-- The quality of the produced partitions has been improved.
-- Added options[2] to specify C or Fortran style numbering.
-
-