STL based LRU cache, any suggestions for improvements?

From:
pmouse@cogeco.ca
Newsgroups:
comp.lang.c++
Date:
30 Apr 2007 19:17:51 -0700
Message-ID:
<1177985871.933804.264390@u30g2000hsc.googlegroups.com>
Hi Guys, I've written a templated lru cache based on the SGI version
of STL. To use the class, simply write:
lru_cache<key_type, data_type, cache_length, custom_container> cache;
cache.insert( key, value );
cache[key2] = value2;

data_type* pvalue3 = cache.get(key3);
if ( pvalue3 == NULL )
{} // cache missing
else
{} // cache hit

data_type must be a class supporting the function dispose(), this is
because for smart pointers, the cache calls this function on the least
recently used data when it's full. for example:

struct myDataType
{
    int value;
    myDataType( int v = 0 ) : value(v) {}
    void dispose() {} // empty functions will be ignored by most
compilers
};
struct mySmartPointerType
{
   int* parray;
   mySmartPointerType( int* a = 0 ) : parray(a) {}
   void dispose() { delete[] parray; }
}

It is important that delete[] parray does not occur in the destructor,
otherwise this type cannot be copied.

my problem: I feel this cache is rather slow. Is there any suggestions
to improve its performance?
I've written a test program:

struct point2d
{
    short x;
    short y;
    point2d( short _x, short _y ) : x(_x), y(_y){}
    bool operator ==( const point2d & rhs ) const
    {
        return (x == rhs.x) && (y == rhs.y);
    }
    bool operator != ( const point2d& rhs ) const
    {
        return ( x!=rhs.x ) || ( y!=rhs.y );
    }
    bool operator < (const point2d & rhs ) const
    {
        return ( x < rhs.x ) || ( x==rhs.x && y < rhs.y ) ;
    }
};

struct basetile
{
    short id;
    char z;
    basetile( int _id = 0, int _z = 0 ) : id(_id), z(_z) {}

    bool operator == ( const basetile& rhs ) const
    {
        return id == rhs.id && z == rhs.z;
    }
    bool operator != ( const basetile& rhs ) const
    {
        return id != rhs.id || z != rhs.z;
    }
    void dispose() {}
} __attribute__((packed)) ;

void cacheTest_perf()
{
    lru_cache<point2d, basetile> cache;
    int count=0;

    srand( 3456789 );
    for ( int i = 0; i < 100000; ++i )
    {
        point2d p(rand()%200, rand()%200);
        basetile& t = cache[p];

        if ( t.id != 0 )
        {
            count++;
        }
        else
        {
            t.id = i % 1024 + 1;
            t.z = 10;
        }
    }

    cout << "total hit " << count << " out of 100000 requests" <<
endl;
}

the test program uses a point2d structure as index, and a 3 byte
packed structure as data type. the result is:

$ time ./testapp
total hit 23574 out of 100000 requests

real 0m0.066s
user 0m0.055s
sys 0m0.002s

i'm pretty sure that's a bad performance for a cache. soo...any
suggestions to speed up the process is welcome!
the source code is included below:

#ifndef LRUCACHE_H_INCLUDED
#define LRUCACHE_H_INCLUDED

#include <iostream>
#include <list>
// #include <map>

#ifdef __GNUC__
    #include <ext/hash_map>
    using namespace __gnu_cxx;
#else
    #include <hash_map>
#endif
using namespace std;

template<class T>
struct GenericHashAdapter
{
    size_t operator()(const T& x) const
    {
        unsigned char* p = (unsigned char*)(&x);
        unsigned int h = 0;
        for ( unsigned int i = 0; i < sizeof(T); ++i )
        {
            h += p[i];
            h += h << 10;
            h ^= h >> 6;
        }
        h += ( h << 3 );
        h ^= ( h >> 11 );
        h += ( h << 15 );
        return(h);
    }
};

template<class T>
struct GenericEqualAdapter
{
    size_t operator() (const T&a, const T&b) const
    {
        return a == b;
    }
};

/*
 * lru cache
 * TElm must support the "dispose()" call
 * the cache will call dispose() on the least recently used element
 * when the cache is full
 */
template <class TKey,
          class TElm,
          int Capacity = 10000,
          // - to use RB Tree instead of hash table: uncomment next
line and comment out the following line.
          // class Container = map<TKey, pair<TElm, typename
list<TKey>::iterator> >
          // - hashtable container:
          class Container = hash_map<TKey, pair<TElm, typename
list<TKey>::iterator>, GenericHashAdapter<TKey>,
GenericEqualAdapter<TKey> >
          >
class lru_cache
{
    typedef TKey key_type;
    typedef TElm data_type;
    typedef TElm& reference;
    typedef TElm* pointer;

    public:
    typedef list<TKey> key_list;
    typedef typename key_list::iterator key_iterator;
    typedef typename Container::iterator item_iterator;
    typedef pair<TElm, key_iterator> mapped_type;
    typedef pair<TKey, mapped_type> value_type;

    lru_cache() : _list(), _map()
    {
    }
    virtual ~lru_cache()
    {
    }

    item_iterator find( const key_type& key )
    {
        return _map.find(key);
    }

    reference insert( const key_type& key, const data_type& data )
    {
        // 1. push key to list
        _list.push_front(key);
        // 2. insert element to map
        pair<item_iterator, bool> ret = _map.insert(
            value_type(key, mapped_type(data, _list.begin()))
        );
        if ( !ret.second )
        {
            // key already exist
            // remove the old key from the list
            // old key is at ret.first->second.second
            _list.erase( ret.first->second.second );
            // also need to update the key iterator reference in the
note
            ret.first->second.second = _list.begin();
        }
        else
        {
            // insert successful
            // is map full?
            if ( _map.size() >= Capacity )
            {
                // get the least recently used key
                item_iterator itr = _map.find(_list.back());
                // dispose data
                itr->second.first.dispose();
                // erase from table
                _map.erase( itr );
                // remove last key from list
                _list.pop_back();
            }
        }
        return ret.first->second.first;
    }
    void update_key( item_iterator& itr )
    {
        _list.erase( itr->second.second );
        _list.push_front( itr->first );
        itr->second.second = _list.begin();
    }

    pointer get( const key_type& key )
    {
        item_iterator itr = _map.find(key);
        if ( itr != _map.end() )
        {
            update_key( itr );
            return &(itr->second.first);
        }
        else
            return NULL;
    }
    pointer look( const key_type& key )
    {
        item_iterator itr = _map.find(key);
        if ( itr != _map.end() )
        {
            return &(itr->second.first);
        }
        else
            return NULL;
    }

    reference operator[](const key_type& key )
    {
        // algorithm:
        // try insert the key
        // if it already exist, then the correct element will be returned
        // otherwise, a new note with default element will be created
and returned
        return insert( key, data_type() );
    }

    pointer operator()(const key_type&key)
    {
        return get(key);
    }

    key_iterator beginkey()
    {
        return _list.begin();
    }
    key_iterator endkey()
    {
        return _list.end();
    }

    private:
    key_list _list;
    Container _map;
};

#endif

Regards,

PQ

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