arm/crypto: Add optimized AES and SHA1 routines

Add assembler versions of AES and SHA1 for ARM platforms.  This has provided
up to a 50% improvement in IPsec/TCP throughout for tunnels using AES128/SHA1.

Platform   CPU SPeed    Endian   Before (bps)   After (bps)   Improvement

IXP425      533 MHz      big     11217042        15566294        ~38%
KS8695      166 MHz     little    3828549         5795373        ~51%

Signed-off-by: David McCullough <ucdevel@gmail.com>
Signed-off-by: faux123 <reioux@gmail.com>
hammerhead defconfig: Enable arm optimized sha1 and aes algo
scheduler: compute time-average nr_running per run-queue

Compute the time-average number of running tasks per run-queue for a
trailing window of a fixed time period. The detla add/sub to the
average value is weighted by the amount of time per nr_running value
relative to the total measurement period.

Change-Id: I076e24ff4ed65bed3b8dd8d2b279a503318071ff
Signed-off-by: Diwakar Tundlam <dtundlam@nvidia.com>
(cherry picked from commit 3a12d7499cee352e8a46eaf700259ba3c733f0e3)
Reviewed-on: http://git-master/r/111635
Reviewed-by: Automatic_Commit_Validation_User
Reviewed-by: Sai Gurrappadi <sgurrappadi@nvidia.com>
Tested-by: Sai Gurrappadi <sgurrappadi@nvidia.com>
Reviewed-by: Peter Boonstoppel <pboonstoppel@nvidia.com>
Reviewed-by: Yu-Huan Hsu <yhsu@nvidia.com>

forward ported to Linux 3.4 for use on S-800

Signed-off-by: Paul Reioux <reioux@gmail.com>
proc: enhance time-average nr_running stats

Add time-average nr_running to loadavg printout

Bug 958978

Change-Id: I5c6904efb52a86f4964eb66c1576fc91f60f5b1d
Signed-off-by: Alex Frid <afrid@nvidia.com>
(cherry picked from commit 86f3642cc44a69d1e4798719bd9182cd6923f526)
Reviewed-on: http://git-master/r/111636
Reviewed-by: Sai Gurrappadi <sgurrappadi@nvidia.com>
Tested-by: Sai Gurrappadi <sgurrappadi@nvidia.com>
Reviewed-by: Automatic_Commit_Validation_User
Reviewed-by: Peter Boonstoppel <pboonstoppel@nvidia.com>
Reviewed-by: Yu-Huan Hsu <yhsu@nvidia.com>
scheduler: Re-compute time-average nr_running on read

Re-compute time-average nr_running when it is read. This would
prevent reading stalled average value if there were no run-queue
changes for a long time. New average value is returned to the reader,
but not stored to avoid concurrent writes. Light-weight sequential
counter synchronization is used to assure data consistency for
re-computing average.

Change-Id: I8e4ea1b28ea00b3ddaf6ef7cdcd27866f87d360b
Signed-off-by: Alex Frid <afrid@nvidia.com>
(cherry picked from commit 527a759d9b40bf57958eb002edd2bb82014dab99)
Reviewed-on: http://git-master/r/111637
Reviewed-by: Sai Gurrappadi <sgurrappadi@nvidia.com>
Tested-by: Sai Gurrappadi <sgurrappadi@nvidia.com>
Reviewed-by: Automatic_Commit_Validation_User
Reviewed-by: Peter Boonstoppel <pboonstoppel@nvidia.com>
Reviewed-by: Yu-Huan Hsu <yhsu@nvidia.com>

forward ported to Linux 3.4 for use on Mako

Signed-off-by: faux123 <reioux@gmail.com>

Conflicts:

	kernel/sched/sched.h
cpufreq: enable sibling cpu policy sharing

disable Qualcomm's cpu policy restore (conflicts with sibling sharing)

Signed-off-by: Paul Reioux <reioux@gmail.com>
cpufreq: ondemand: Set MIN_LATENCY_MULTIPLIER to 20

Currently MIN_LATENCY_MULTIPLIER is set defined as 100 and so on a system with
transition latency of 1 ms, the minimum sampling time comes to be around 100 ms.
That is quite big if you want to get better performance for your system.

Redefine MIN_LATENCY_MULTIPLIER to 20 so that we can support 20ms sampling rate
for such platforms.

Signed-off-by: faux123 <reioux@gmail.com>
cpufreq: conservative: Set MIN_LATENCY_MULTIPLIER to 20

Currently MIN_LATENCY_MULTIPLIER is set defined as 100 and so on a system with
transition latency of 1 ms, the minimum sampling time comes to be around 100 ms.
That is quite big if you want to get better performance for your system.

Redefine MIN_LATENCY_MULTIPLIER to 20 so that we can support 20ms sampling rate
for such platforms.

Signed-off-by: faux123 <reioux@gmail.com>
overall_stats: add overall stats for all available cores

this is much more accurate stats tracking than just displaying core 0's
stats from most other kernels

Signed-off-by: faux123 <reioux@gmail.com>
overall_stats: make dual/quad core stats configurable via meunconfig

Signed-off-by: faux123 <reioux@gmail.com>
hammerhead defconfig: Enable newly added quadcore stats
overall_stats: forward port to kernel 3.4+

by replacing deprecated functions with native add/sub for 64bit data types

Signed-off-by: faux123 <reioux@gmail.com>
block: cgroups, kconfig, build bits for BFQ-v6-3.4

Update Kconfig.iosched and do the related Makefile changes to include
kernel configuration options for BFQ. Also add the bfqio controller
to the cgroups subsystem.

Signed-off-by: Paolo Valente <paolo.valente@unimore.it>
Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com>

Signed-off-by: Paul Reioux <reioux@gmail.com>
block: introduce the BFQ-v6 I/O sched for 3.4

Add the BFQ-v6 I/O scheduler to 3.4.
The general structure is borrowed from CFQ, as much code. A (bfq_)queue is
associated to each task doing I/O on a device, and each time a scheduling
decision has to be made a queue is selected and served until it expires.

    - Slices are given in the service domain: tasks are assigned budgets,
      measured in number of sectors. Once got the disk, a task must
      however consume its assigned budget within a configurable maximum time
      (by default, the maximum possible value of the budgets is automatically
      computed to comply with this timeout). This allows the desired latency
      vs "throughput boosting" tradeoff to be set.

    - Budgets are scheduled according to a variant of WF2Q+, implemented
      using an augmented rb-tree to take eligibility into account while
      preserving an O(log N) overall complexity.

    - A low-latency tunable is provided; if enabled, both interactive and soft
      real-time applications are guaranteed very low latency.

    - Latency guarantees are preserved also in presence of NCQ.

    - Also with flash-based devices, a high throughput is achieved while
      still preserving latency guarantees.

    - A useful feature borrowed from CFQ: static fallback queue for OOM.

    - Differently from CFQ, BFQ uses a unified mechanism (Early Queue Merge,
      EQM) to get a sequential read pattern, and hence a high throughput,
      with any set of processes performing interleaved I/O. EQM also
      preserves low latency. The code for detecting whether two queues have
      to be merged is a slightly modified version of the CFQ code for
      detecting whether two queues belong to cooperating processes and whether
      the service of a queue should be preempted to boost the throughput.

    - BFQ supports full hierarchical scheduling, exporting a cgroups
      interface.  Each node has a full scheduler, so each group can
      be assigned its own ioprio (mapped to a weight, see next point)
      and an ioprio_class.

    - If the cgroups interface is used, weights can be explictly assigned,
      otherwise ioprio values are mapped to weights using the relation
      weight = IOPRIO_BE_NR - ioprio.

    - ioprio classes are served in strict priority order, i.e., lower
      priority queues are not served as long as there are higher priority
      queues.  Among queues in the same class the bandwidth is distributed
      in proportion to the weight of each queue. A very thin extra bandwidth
      is however guaranteed to the Idle class, to prevent it from starving.

Signed-off-by: Paolo Valente <paolo.valente@unimore.it>
Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com>
block: fiops ioscheduler core

FIOPS (Fair IOPS) ioscheduler is IOPS based ioscheduler, so only targets
for drive without I/O seek. It's quite similar like CFQ, but the dispatch
decision is made according to IOPS instead of slice.

The algorithm is simple. Drive has a service tree, and each task lives in
the tree. The key into the tree is called vios (virtual I/O). Every request
has vios, which is calculated according to its ioprio, request size and so
on. Task's vios is the sum of vios of all requests it dispatches. FIOPS
always selects task with minimum vios in the service tree and let the task
dispatch request. The dispatched request's vios is then added to the task's
vios and the task is repositioned in the sevice tree.

Unlike CFQ, FIOPS doesn't have separate sync/async queues, because with I/O
less writeback, usually a task can only dispatch either sync or async requests.
Bias read or write request can still be done with read/write scale.

One issue is if workload iodepth is lower than drive queue_depth, IOPS
share of a task might not be strictly according to its priority, request
Bias read or write request can still be done with read/write scale.

One issue is if workload iodepth is lower than drive queue_depth, IOPS
share of a task might not be strictly according to its priority, request
size and so on. In this case, the drive is in idle actually. Solving the
problem need make drive idle, so impact performance. I believe CFQ isn't
completely fair between tasks in such case too.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>

Signed-off-by: Paul Reioux <reioux@gmail.com>
block: fiops read/write request scale

read/write speed of Flash based storage usually is different. For example,
in my SSD maxium thoughput of read is about 3 times faster than that of
write. Add a scale to differenate read and write. Also add a tunable, so
user can assign different scale for read and write.

By default, the scale is 1:1, which means the scale is a noop.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>
block: fiops sync/async scale

CFQ gives 2.5 times more share to sync workload. This matches CFQ.

Note this is different with the read/write scale. We have 3 types of
requests:
1. read
2. sync write
3. write
CFQ doesn't differentitate type 1 and 2, but request cost of 1 and 2
are usually different for flash based storage. So we have both sync/async
and read/write scale here.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>
block: fiops add ioprio support

Add CFQ-like ioprio support. Priority A will get 20% more share than priority
A+1, which matches CFQ.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>
block: fiops preserve vios key for deep queue depth workload

If the task has running request, even it's added into service tree newly,
we preserve its vios key, so it will not lost its share. This should work
for task driving big queue depth. For single depth task, there is no approach
to preserve its vios key.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>
block: fiops bias sync workload

If there are async requests running, delay async workload. Otherwise
async workload (usually very deep iodepth) will use all queue iodepth
and later sync requests will get long delayed. The idea is from CFQ.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>