DirectX11 Radix Sort - sorting

Question

I'm struggling to implement an efficient Radix Sort in DirectX11.
I need to quickly sort no more than 256K items.
It doesn't need to be in-place.
Using CUDA / OpenCL is not an option.
I have it almost working, but it has a few problems:
The histogram generation is quite fast, but still takes much longer than quoted figures online
On subsequent sort passes, the order of keys whose lower bits are identical, is not preserved, due to the InterlockedAdd on the histogram buffer in cp_sort (see below)
cp_sort is really slow, due to global memory access on that same InterlockedAdd
I've been trying to understand how I can fix this, based on algorithms online, but I can't seem to understand them.
Here are my 3 kernels. (it's for a billboarded particle system, so the term 'quad' just refers to an item to be sorted)
// 8 bits per radix
#define NUM_RADIXES 4
#define RADIX_SIZE 256
// buffers
StructuredBuffer<Quad> g_particleQuadBuffer : register( t20 );
RWStructuredBuffer<uint> g_indexBuffer : register( u0 );
RWStructuredBuffer<uint> g_histogram : register( u1 );
RWStructuredBuffer<uint> g_indexOutBuffer : register( u2 );
// quad buffer counter
cbuffer BufferCounter : register( b12 )
{
uint numQuads;
uint pad[ 3 ];
}
// on-chip memory for fast histogram calculation
#define SHARED_MEM_PADDING 8 // to try and reduce bank conflicts
groupshared uint g_localHistogram[ NUM_RADIXES * RADIX_SIZE * SHARED_MEM_PADDING ];
// convert a float to a sortable int, assuming all inputs are negative
uint floatToUInt( float input )
{
return 0xffffffff - asuint( input );
}
// initialise the indices, and build the histograms (dispatched with numQuads / ( NUM_RADIXES * RADIX_SIZE ))
[numthreads( NUM_RADIXES * RADIX_SIZE, 1, 1 )]
void cp_histogram( uint3 groupID : SV_GroupID, uint groupIndex : SV_GroupIndex )
{
// initialise local histogram
g_localHistogram[ groupIndex * SHARED_MEM_PADDING ] = 0;
GroupMemoryBarrierWithGroupSync();
// check within range
uint quadIndex = ( groupID.x * NUM_RADIXES * RADIX_SIZE ) + groupIndex;
if ( quadIndex < numQuads )
{
// initialise index
g_indexBuffer[ quadIndex ] = quadIndex;
// floating point to sortable uint
uint value = floatToUInt( g_particleQuadBuffer[ quadIndex ].v[ 0 ].projected.z );
// build 8-bit histograms
uint value0 = ( value ) & 0xff;
uint value1 = ( value >> 8 ) & 0xff;
uint value2 = ( value >> 16 ) & 0xff;
uint value3 = ( value >> 24 );
InterlockedAdd( g_localHistogram[ ( value0 ) * SHARED_MEM_PADDING ], 1 );
InterlockedAdd( g_localHistogram[ ( value1 + 256 ) * SHARED_MEM_PADDING ], 1 );
InterlockedAdd( g_localHistogram[ ( value2 + 512 ) * SHARED_MEM_PADDING ], 1 );
InterlockedAdd( g_localHistogram[ ( value3 + 768 ) * SHARED_MEM_PADDING ], 1 );
}
// write back to histogram
GroupMemoryBarrierWithGroupSync();
InterlockedAdd( g_histogram[ groupIndex ], g_localHistogram[ groupIndex * SHARED_MEM_PADDING ] );
}
// build the offsets based on histograms (dispatched with 1)
// NOTE: I know this could be more efficient, but from my profiling, its time is negligible compared to the other 2 stages, and I can optimise this separately using a parallel prefix sum if I need to
[numthreads( NUM_RADIXES, 1, 1 )]
void cp_offsets( uint groupIndex : SV_GroupIndex )
{
uint sum = 0;
uint base = ( groupIndex * RADIX_SIZE );
for ( uint i = 0; i < RADIX_SIZE; i++ )
{
uint tempSum = g_histogram[ base + i ] + sum;
g_histogram[ base + i ] = sum;
sum = tempSum;
}
}
// move the data (dispatched with numQuads / ( NUM_RADIXES * RADIX_SIZE ))
uint currentRadix;
[numthreads( NUM_RADIXES * RADIX_SIZE, 1, 1 )]
void cp_sort( uint3 groupID : SV_GroupID, uint groupIndex : SV_GroupIndex )
{
// check within range
uint quadIndex = ( groupID.x * NUM_RADIXES * RADIX_SIZE ) + groupIndex;
if ( quadIndex < numQuads )
{
uint fi = g_indexBuffer[ quadIndex ];
uint depth = floatToUInt( g_particleQuadBuffer[ fi ].v[ 0 ].projected.z );
uint radix = currentRadix;
uint pos = ( depth >> ( radix * 8 ) ) & 0xff;
uint writePosition;
InterlockedAdd( g_histogram[ radix * RADIX_SIZE + pos ], 1, writePosition );
g_indexOutBuffer[ writePosition ] = fi;
}
}
Can anyone offer any help on how to fix/optimise this?
I'd love to understand what some of the more complex algorithms for GPU Radix sorting are actually doing!
Thanks!