ClickHouse IOUring

  1. iouring fs插桩bpf uring context,到最底下的nvme层dispatch一段batching读的代码。iouring sock xdp一个请求接一个请求dispatch。穿透两层,一个是iouring层,另外是xdp和xrp插桩的地方层。
  2. MergeTree到ReplicatedMergeTree,想用iouring batch socket接read的一些请求。

Qemu CXL type 1 emulation proposal

Introduction to CXL Type 1

Guided Usecase

[1] and [2] are just Qemu's implementation of dm-crypto for LUKS; every device mapper over a physical block device will require a key and a crypto accelerator or software crypto implementation to decrypt to get the data. We implement a crypto accelerator with CXL type 1 semantics over a framework of virtio-crypto-pci. We want to emulate the mal state or unplug the crypto device; the kernel will get ATS bit DMAed data and resume CPU software crypto implementation.

Device emulation

DMA and access memory

Create a CacheMemRegion that maps a specific SPP region for one mapping of a bunch of CXL.cache caches on a CXL device.

Crypto operations

When calling crypto operations in the kernel, we actually offload the encrypt/decrypt operations to the type 1 accelerator through CXL.io, which tells the device cast on operation on arbitrary SPP. The accelerator will first take ownership of arbitrary SPP in the CacheMemRegion and notify the host. Eventually, the host will get the shared state of the SPP's cacheline.

Cache coherency emulation

struct D2HDataReq = {
    D2H DataHeader 24b;
    opcode 4b;
    CXL.cache Channel Crediting;
}
struct CXLCache = { 
    64Byte Data; 
    MESI 4 bit;
    ATS 64 bit;
    [D2HDataReq;n] remaining bit;
}

Use metadata with Intel SPP writes protection support and mark the access to an arbitrary cacheline in the SPP. We need to perform all the transaction descriptions and queuing in the 64-byte residue data in the SPP. The arbitrary operation, according to the queue, will have the effect of MESI bit change and update writes protection for the subpage and root complex or other side effects like switches changing.

The host's and device's requests are not scheduled FIFO, but the host's seeing the data will have better priority. So, the H2D requirement will be consumed first and done in D2H FIFO. All the operations follow interface operations to CXLCache.

Taking exclusiveness-able

We mark the Transportation ATS bit as exclusiveness-able. and copy the cacheline in another map; once emulated and unplugged, the cacheline is copied back for further operation of the kernel to resume software crypto calculation.

How to emulate the eviction

We have two proposals

  1. pebs to watch the cache eviction of the physical address of an SPP
  2. Use sub-page pin page_get_fast to pin to a physical address within the last-level cache. [7]

The code is currently developed at https://github.com/SlugLab/Drywall/ and pitfalls refer to https://asplos.dev/wordpress/2023/11/27/intel-sub-page-write-protection-cai-keng/

Alternative implementation

Maybe we should limit the operation of the address from CXL.cache, write a memory santinization assisted JIT compiler and use LAM instead.

Reference

  1. https://www.os.ecc.u-tokyo.ac.jp/papers/2021-cloud-ozawa.pdf
  2. https://people.redhat.com/berrange/kvm-forum-2016/kvm-forum-2016-security.pdf
  3. https://yhbt.net/lore/all/[email protected]/T/
  4. https://privatewiki.opnfv.org/_media/dpacc/a_new_framework_of_cryptography_virtio_driver.pdf
  5. https://github.com/youcan64/spp_patched_qemu
  6. https://github.com/youcan64/spp_patched_linux
  7. https://people.kth.se/~farshin/documents/slice-aware-eurosys19.pdf

[Computer Architecture] Sniper lab2

The code is at http://victoryang00.xyz:5012/victoryang/sniper_test/tree/lab2.

The lab was to implement the Hash perceptron according to the paper "Dynamic-Branch-Prediction-with-Perceptrons".

The cache implementation is in common/performance_model/branch_predictor.cc

#include "perceptron_branch_predictor.h"
...
else if (type == "perceptron")
{
   return new PerceptronBranchPredictor("branch_predictor", core_id);
}

The modification in the cfg

[perf_model/branch_predictor]
type = perceptron    # add Perceptron
mispredict_penalty=8 # Reflects just the front-end portion (approx) of the penalty for Interval Simulation

The constructor is passed to the perceptron_branch_predictor.h, we have to maintain the member variable as below:

struct PHT {
        bool taken;     //jump or not
        IntPtr target;  //64 history target address. 
        PHT(bool bt, IntPtr it) : taken(bt), target(it) {}
    };//The pattern history table, set like a round linked list to avoid the memory copy

//To store the history result.
		SInt32 history_sum;
    UInt32 history_bias_index;
    std::vector<UInt32> history_indexes;
    std::vector<PHT> history_path;
    UInt32 history_path_index;

    std::vector<PHT> path;
    UInt32 path_index;

    UInt32 path_length;
    UInt32 num_entries;
    UInt32 num_bias_entries;//1024
    UInt32 weight_bits;
    std::vector<std::vector<SInt32>> weights;
    std::vector<SInt32> perceptron;           //Update the perceptron table to 1024 entries
    UInt32 theta;															//Threshold, to determine the train is good
    SInt64 count;														  //To store the count
    UInt32 block_size;											  //block sides initialize to perseption

    // initialize the branch history register and perceptron table to 0
PerceptronBranchPredictor::PerceptronBranchPredictor(String name, core_id_t core_id)
    : BranchPredictor(name, core_id), history_sum(0), history_bias_index(0), history_indexes(64 / 8, 0), history_path(64, PHT(false, 0)), history_path_index(0), path(64, PHT(false, 0)), path_index(0), path_length(64), num_entries(256), num_bias_entries(1024), weight_bits(7), weights(256, std::vector<SInt32>(64, 0)), perceptron(1024, 0), theta(296.64), count(0), block_size(8), coefficients(64, 0)

The prediction method

The Base neral predictor is based on the equation $y=w_{0}+\sum_{i=1}^{n} x_{i} w_{i}$, We have that

// if the prediction is wrong or the weight value is smaller than the threshold THETA, 
// then update the weight  to train the perceptron
weights = floor(1.93 * blocksize + 14)

Hashed Perceptron Multiple

  1. set of branches assigned to same weights
  2. More than one set of information can be used to hash into the weight table
  3. Diferent hashing function can be used such as : XOR, Concatenation etc.
  4. Diferent indices can be assigned to diferent weights

Algorithm

The input is ip, and first come to a computation of (ip >> 4) % num_bias_entries;

This give the seed to the weight table lines:

table of perception will get update to te coefficient*weight and

and weight table will update to weights[index[i]][i*8...(i+1)*8]*coefficients[i*8...(i+1)*8]*h

if the result >0 the prediction is right then jump, or not jump.

Then update the history and insert the round linked list.

bool PerceptronBranchPredictor::predict(IntPtr ip, IntPtr target)
{
    double sum = 0;
  //hash method
    UInt32 bias_index = (ip >> 4) % num_bias_entries;

    sum += bias_coefficient * perceptron[bias_index];
    history_bias_index = bias_index;
  //update the weight
    for (UInt32 i = 0; i < path_length; i += block_size)
    {
        IntPtr z = ip >> 4;
        for (UInt32 j = 0; j < block_size; j++)
        {
            z ^= (path[(path_index - i - j + path_length - 1) % path_length].target >> 4);
        }
        UInt32 index = z % num_entries;
        history_indexes[i / block_size] = index;
        for (UInt32 j = 0; j < block_size; j++)
        {
            SInt32 h = path[(path_index - i - j + path_length - 1) % path_length].taken ? 1 : -1;
            sum += h * weights[index][i + j] * coefficients[i + j];
        }
    }
    bool result = ((SInt32)sum) >= 0;
    history_sum = (SInt32)sum;

    history_path.assign(path.begin(), path.end());
    history_path_index = path_index;
    path[path_index] = PHT(result, target);
    path_index = (path_index + 1) % path_length;

    return result;
}

The train process

Algorithm

Input parameters: predicted, actual, ip
auxiliary parameters: history_sum(predicted value), history_bias_index, history_indexes(index). history_path(history), history_path_index(history subscript)

for each bit in parallel
	if t=xi then
  		wi=wi+1
    else
    	wi=wi-1
    end if

Realization

for (UInt32 i = 0; i < path_length; i += block_size)
        {
            index = history_indexes[i / block_size];
            for (UInt32 j = 0; j < block_size; j++)
            {
                bool taken = history_path[(history_path_index - i - j + path_length - 1) % path_length].taken;
                if (taken == actual)
                {
                    if (weights[index][i + j] < (1 << weight_bits) - 1)
                        weights[index][i + j]++;
                }
                else
                {
                    if (weights[index][i + j] > -(1 << weight_bits))
                        weights[index][i + j]--;
                }
            }
        }

The result

FFT with Blocking Transpose
   1024 Complex Doubles
   1 Processors
   65536 Cache lines
   16 Byte line size
   4096 Bytes per page
pentium_m perceptron
num correct 63112 65612
num incorrect 4342 2112
IPC 1.36 1.37
mpki 2.71 1.39
misprediction rate 6.436% 3.118%
Elapsed time 4.46s 5.23s
cycles 1.2M 1.2M
Instructions 1.6M 1.6M

Reference

  1. https://github.com/ChrsMark/BranchPredictors

[Computer Architecture] Sniper lab3

The code can be viewed on http://victoryang00.xyz:5012/victoryang/sniper_test/tree/lab3

false sharing

For the false sharing test cases. We've given the lab3 cfg file that the cache line is 64B. So that we just need to set the false sharing variable under that cache size.

In the false_sharing_bad.c, we open 2 thread to store global variable results with first part th1 visits and the second part th2 visits.

void* myfunc(void *args){
    int i;
    MY_ARGS* my_args=(MY_ARGS*)args;
    int first = my_args->first;
    int last = my_args->last;
    int id = my_args->id;
    // int s=0;
    for (i=first;i<last;i++){
        results[id]=results[id]+arr[i];
    }

    return NULL;
}

The result of this in the sniper.

[SNIPER] Warning: Unable to use physical addresses for shared memory simulation.
[SNIPER] Start
[SNIPER] --------------------------------------------------------------------------------
[SNIPER] Sniper using SIFT/trace-driven frontend
[SNIPER] Running full application in DETAILED mode
[SNIPER] --------------------------------------------------------------------------------
[SNIPER] Enabling performance models
[SNIPER] Setting instrumentation mode to DETAILED
[RECORD-TRACE] Using the Pin frontend (sift/recorder)
[TRACE:1] -- DONE --
[TRACE:2] -- DONE --
s1 = 5003015
s2= 5005373
s1+s2= 10008388
[TRACE:0] -- DONE --
[SNIPER] Disabling performance models
[SNIPER] Leaving ROI after 627.88 seconds
[SNIPER] Simulated 445.0M instructions, 192.9M cycles, 2.31 IPC
[SNIPER] Simulation speed 708.8 KIPS (177.2 KIPS / target core - 5643.3ns/instr)
[SNIPER] Setting instrumentation mode to FAST_FORWARD
[SNIPER] End
[SNIPER] Elapsed time: 627.79 seconds

In the false_sharing.c, we open 2 thread to store different local variable s with th1 visits and th2 visits.

void* myfunc(void *args){
    int i;
    MY_ARGS* my_args=(MY_ARGS*)args;
    int first = my_args->first;
    int last = my_args->last;
    // int id = my_args->id;
    int s=0;
    for (i=first;i<last;i++){
        s=s+arr[i];
    }

    my_args->result=s;
    return NULL;
}

The result of this in the sniper.

[SNIPER] Warning: Unable to use physical addresses for shared memory simulation.
[SNIPER] Start
[SNIPER] --------------------------------------------------------------------------------
[SNIPER] Sniper using SIFT/trace-driven frontend
[SNIPER] Running full application in DETAILED mode
[SNIPER] --------------------------------------------------------------------------------
[SNIPER] Enabling performance models
[SNIPER] Setting instrumentation mode to DETAILED
[RECORD-TRACE] Using the Pin frontend (sift/recorder)
[TRACE:2] -- DONE --
[TRACE:1] -- DONE --
s1 = 5003015
s2= 5003015
s1+s2= 10006030
[TRACE:0] -- DONE --
[SNIPER] Disabling performance models
[SNIPER] Leaving ROI after 533.95 seconds
[SNIPER] Simulated 415.1M instructions, 182.1M cycles, 2.28 IPC
[SNIPER] Simulation speed 777.3 KIPS (194.3 KIPS / target core - 5145.9ns/instr)
[SNIPER] Setting instrumentation mode to FAST_FORWARD
[SNIPER] End
[SNIPER] Elapsed time: 533.99 seconds

The reason of false sharing:

Every time the thread may let the results to get into the CPU cache.

The Cache may check whether the cache part and memory are the same or not, thus trigger the latency, which is false sharing.

The solution of the false sharing:

Just let the adjacent data's distance larger than the one cache line, say 64B. so set FALSE_ARR to 200000.

The result changed to:

[SNIPER] Warning: Unable to use physical addresses for shared memory simulation.
[SNIPER] Start
[SNIPER] --------------------------------------------------------------------------------
[SNIPER] Sniper using SIFT/trace-driven frontend
[SNIPER] Running full application in DETAILED mode
[SNIPER] --------------------------------------------------------------------------------
[SNIPER] Enabling performance models
[SNIPER] Setting instrumentation mode to DETAILED
[RECORD-TRACE] Using the Pin frontend (sift/recorder)
[TRACE:1] -- DONE --
[TRACE:2] -- DONE --
s1 = 5003015
s2= 5005373
s1+s2= 10008388
[TRACE:0] -- DONE --
[SNIPER] Disabling performance models
[SNIPER] Leaving ROI after 512.28 seconds
[SNIPER] Simulated 445.1M instructions, 158.1M cycles, 2.82 IPC
[SNIPER] Simulation speed 868.8 KIPS (217.2 KIPS / target core - 4604.2ns/instr)
[SNIPER] Setting instrumentation mode to FAST_FORWARD
[SNIPER] End
[SNIPER] Elapsed time: 512.22 seconds

multi-level cache

The non-inclusive cach is to remove the back-invalidation and fix one.

Then I found https://groups.google.com/g/snipersim/c/_NJu8DXCVVs/m/uL3Vo24OAAAJ. That the non-inclusive cache intends to directly write back to the memory. We have to fix some bugs for inclusive cache during the L1 eviction and L2 have to evict, but L2 may not be found, thus the L1 should WB to memory, in this time.

I add a new configuration in the cfg. to make the L1 non-inclusive or not and deployed different Protocol in cfg.

[caching_protocol]
type = parametric_dram_directory_msi
variant = mesif                           # msi, mesi or mesif

I didn't do a result with a small granularity but with lock_add lock_fill_bucket reader_writer and bfs, I got num of write backs and some WB test for inclusive and non inclusive caches.

Test

Lock Add: In this test all the threads try to add ‘1’ to a global counter using locks. We see lower number of memory writebacks in MOSI because of the presence of the owner state.

Lock Fill Bucket: This tests makes buckets for numbers, so that it can count how many times each number is present in the array. This is done using locks. The dragon protocol is performing much worse here compared to others. This is probably because updates to the buckets are not always needed by other processors, hence the updates on the writes do not help.

Result

Protocol
Protocol\IPC lock_add lock_fill_bucket reader_writer bfs
MSI 1.31 1.32 1.27 1.39
MESI 1.35 1.36 1.29 1.39
MESIF 1.35 1.36 1.30 1.39

The MESIF protocol enhances the performance for teh multicore systems. MESIF protocal enhances the performance for multicore system. It may be aware of the micro arch.

CPI stack

The CPI stack can quantify the cycle gone due to memory branch or sync. literally all of the protocal has similar graph as above. The MESIF shows the lowest time in mem and sync.

Protocol\L2 miss rate lock_add lock_fill_bucket reader_writer bfs
MSI 49.18 20.13 27.12 42.24
MSI (with non-inclusive cache) 47.98 20.01 29.12 42.13
MESI 46.21 21.13 31.29 41.31
MESI (with non-inclusive cache) 45.13 21.41 26.41 42.15
MESIF 45.71 20.12 25.14 41.39
MESIF (with non-inclusive cache) 46.35 23.14 24.14 41.13

The non-inclusive cache have a better score than inclusive ones in L2 miss rate and MESIF & MESI are better than the MSI.

Summary

Interesting Conclusions

Adding ‘E’ state:

To measure the performance differences caused by adding the Exclusive state to the protocols, we can look at the differences in metrics in MSI vs MESI and MESIF. The main benefit of the Exclusive state is in reducing the number of snooping bus transactions required. If we consider a parallel program where each thread works on a chunk of an array and updates only that chunk, or if we assume a sequential program that has a single thread, then in these cases, there will be a lot of cases where a program is reading a cacheline and updating it. In MSI, this would translate to first loading the cacheline using a BusRd moving to the S state, and then performing a BusRdX and moving to the M state. This requires two snooping bus transactions. In the case of MESI, this can be done in a single transaction. The cache would perform a BusRd moving to the E state and then since no other cache has the cacheline, there is no need of a BusRdX transaction to move to the M state. It can just silently change the status of the cacheline to Modified.
This gives a significant boost in programs which access and modify unique memory addresses.

Write-Invalidation vs Write-Update

Since we have implemented both write invalidation and write update protocols, our simulator can also tell whether for a given program or memory trace, write invalidation protocols will be better or write update.
For a write-invalidation protocol, when a processor writes to a memory location, other processor caches need to invalidate that cacheline. In a write-update protocol, instead of invalidating the cachelines, it sends the updated cacheline to the other caches. Therefore, in cases where the other processors will need to read those values in the future, write-update performs well, but if the other processors are not going to be needing those values, then the updates are not going to be of any use, and will just cause extra bus transactions. Therefore, the effects of the protocol would be completely dependent.
From our tests, we saw lesser number of bus transactions This would explain why updating rather than invalidating reduced the number of bus transactions.

MOESI (Unchecked)

I refer to the graph of

The code is in dir_moesi directory with one state added.

I just make it runnable and not yet tested.

Reference

  1. https://www.youtube.com/watch?v=3DoBCs7Lyv4
  2. https://kshitizdange.github.io/