diff --git a/src/nvtt/CMakeLists.txt b/src/nvtt/CMakeLists.txt
index 501ce4a..7b92379 100644
--- a/src/nvtt/CMakeLists.txt
+++ b/src/nvtt/CMakeLists.txt
@@ -5,7 +5,6 @@ ADD_SUBDIRECTORY(squish)
 SET(NVTT_SRCS
     nvtt.h nvtt.cpp
     nvtt_wrapper.h nvtt_wrapper.cpp
-    ClusterFit.h ClusterFit.cpp
     Compressor.h
     BlockCompressor.h BlockCompressor.cpp
     CompressorDX9.h CompressorDX9.cpp
diff --git a/src/nvtt/ClusterFit.cpp b/src/nvtt/ClusterFit.cpp
deleted file mode 100644
index 4e8728e..0000000
--- a/src/nvtt/ClusterFit.cpp
+++ /dev/null
@@ -1,617 +0,0 @@
-// MIT license see full LICENSE text at end of file
-
-#include "ClusterFit.h"
-#include "nvmath/Vector.inl"
-
-#include <float.h> // FLT_MAX
-
-using namespace nv;
-
-
-static Vector3 computeCentroid(int n, const Vector3 *__restrict points, const float *__restrict weights, Vector3::Arg metric)
-{
-    Vector3 centroid(0.0f);
-    float total = 0.0f;
-
-    for (int i = 0; i < n; i++)
-    {
-        total += weights[i];
-        centroid += weights[i] * points[i];
-    }
-    centroid *= (1.0f / total);
-
-    return centroid;
-}
-
-static Vector3 computeCovariance(int n, const Vector3 *__restrict points, const float *__restrict weights, Vector3::Arg metric, float *__restrict covariance)
-{
-    // compute the centroid
-    Vector3 centroid = computeCentroid(n, points, weights, metric);
-
-    // compute covariance matrix
-    for (int i = 0; i < 6; i++)
-    {
-        covariance[i] = 0.0f;
-    }
-
-    for (int i = 0; i < n; i++)
-    {
-        Vector3 a = (points[i] - centroid) * metric;    // @@ I think weight should be squared, but that seems to increase the error slightly.
-        Vector3 b = weights[i] * a;
-
-        covariance[0] += a.x * b.x;
-        covariance[1] += a.x * b.y;
-        covariance[2] += a.x * b.z;
-        covariance[3] += a.y * b.y;
-        covariance[4] += a.y * b.z;
-        covariance[5] += a.z * b.z;
-    }
-
-    return centroid;
-}
-
-// @@ We should be able to do something cheaper...
-static Vector3 estimatePrincipalComponent(const float * __restrict matrix)
-{
-    const Vector3 row0(matrix[0], matrix[1], matrix[2]);
-    const Vector3 row1(matrix[1], matrix[3], matrix[4]);
-    const Vector3 row2(matrix[2], matrix[4], matrix[5]);
-
-    float r0 = lengthSquared(row0);
-    float r1 = lengthSquared(row1);
-    float r2 = lengthSquared(row2);
-
-    if (r0 > r1 && r0 > r2) return row0;
-    if (r1 > r2) return row1;
-    return row2;
-}
-
-static inline Vector3 firstEigenVector_PowerMethod(const float *__restrict matrix)
-{
-    if (matrix[0] == 0 && matrix[3] == 0 && matrix[5] == 0)
-    {
-        return Vector3(0.0f);
-    }
-
-    Vector3 v = estimatePrincipalComponent(matrix);
-
-    const int NUM = 8;
-    for (int i = 0; i < NUM; i++)
-    {
-        float x = v.x * matrix[0] + v.y * matrix[1] + v.z * matrix[2];
-        float y = v.x * matrix[1] + v.y * matrix[3] + v.z * matrix[4];
-        float z = v.x * matrix[2] + v.y * matrix[4] + v.z * matrix[5];
-
-        float norm = max(max(x, y), z);
-
-        v = Vector3(x, y, z) * (1.0f / norm);
-    }
-
-    return v;
-}
-
-static Vector3 computePrincipalComponent_PowerMethod(int n, const Vector3 *__restrict points, const float *__restrict weights, Vector3::Arg metric)
-{
-    float matrix[6];
-    computeCovariance(n, points, weights, metric, matrix);
-
-    return firstEigenVector_PowerMethod(matrix);
-}
-
-void ClusterFit::setColorSet(const Vector3 * colors, const float * weights, int count)
-{
-    // initialise the best error
-#if NVTT_USE_SIMD
-    m_besterror = SimdVector( FLT_MAX );
-    Vector3 metric = m_metric.toVector3();
-#else
-    m_besterror = FLT_MAX;
-    Vector3 metric = m_metric;
-#endif
-
-    m_count = count;
-
-    // I've tried using a lower quality approximation of the principal direction, but the best fit line seems to produce best results.
-    Vector3 principal = computePrincipalComponent_PowerMethod(count, colors, weights, metric);
-
-    // build the list of values
-    int order[16];
-    float dps[16];
-    for (uint i = 0; i < m_count; ++i)
-    {
-        dps[i] = dot(colors[i], principal);
-        order[i] = i;
-    }
-
-    // stable sort
-    for (uint i = 0; i < m_count; ++i)
-    {
-        for (uint j = i; j > 0 && dps[j] < dps[j - 1]; --j)
-        {
-            swap(dps[j], dps[j - 1]);
-            swap(order[j], order[j - 1]);
-        }
-    }
-
-    // weight all the points
-#if NVTT_USE_SIMD
-    m_xxsum = SimdVector( 0.0f );
-    m_xsum = SimdVector( 0.0f );
-#else
-    m_xxsum = Vector3(0.0f);
-    m_xsum = Vector3(0.0f);
-    m_wsum = 0.0f;
-#endif
-	
-    for (uint i = 0; i < m_count; ++i)
-    {
-        int p = order[i];
-#if NVTT_USE_SIMD
-        NV_ALIGN_16 Vector4 tmp(colors[p], 1);
-        m_weighted[i] = SimdVector(tmp.component) * SimdVector(weights[p]);
-        m_xxsum += m_weighted[i] * m_weighted[i];
-        m_xsum += m_weighted[i];
-#else
-        m_weighted[i] = colors[p] * weights[p];
-        m_xxsum += m_weighted[i] * m_weighted[i];
-        m_xsum += m_weighted[i];
-        m_weights[i] = weights[p];
-        m_wsum += m_weights[i];
-#endif
-    }
-}
-
-
-
-void ClusterFit::setColorWeights(Vector4::Arg w)
-{
-#if NVTT_USE_SIMD
-    NV_ALIGN_16 Vector4 tmp(w.xyz(), 1);
-    m_metric = SimdVector(tmp.component);
-#else
-    m_metric = w.xyz();
-#endif
-    m_metricSqr = m_metric * m_metric;
-}
-
-float ClusterFit::bestError() const
-{
-#if NVTT_USE_SIMD
-    SimdVector x = m_xxsum * m_metricSqr;
-    SimdVector error = m_besterror + x.splatX() + x.splatY() + x.splatZ();
-    return error.toFloat();
-#else
-    return m_besterror + dot(m_xxsum, m_metricSqr);
-#endif
-
-}
-
-#if NVTT_USE_SIMD
-
-bool ClusterFit::compress3( Vector3 * start, Vector3 * end )
-{
-    const int count = m_count;
-    const SimdVector one = SimdVector(1.0f);
-    const SimdVector zero = SimdVector(0.0f);
-    const SimdVector half(0.5f, 0.5f, 0.5f, 0.25f);
-    const SimdVector two = SimdVector(2.0);
-    const SimdVector grid( 31.0f, 63.0f, 31.0f, 0.0f );
-    const SimdVector gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f, 0.0f );
-
-    // declare variables
-    SimdVector beststart = SimdVector( 0.0f );
-    SimdVector bestend = SimdVector( 0.0f );
-    SimdVector besterror = SimdVector( FLT_MAX );
-
-    SimdVector x0 = zero;
-
-    // check all possible clusters for this total order
-    for (int c0 = 0; c0 <= count; c0++)
-    {
-        SimdVector x1 = zero;
-
-        for (int c1 = 0; c1 <= count-c0; c1++)
-        {
-            const SimdVector x2 = m_xsum - x1 - x0;
-
-            //Vector3 alphax_sum = x0 + x1 * 0.5f;
-            //float alpha2_sum = w0 + w1 * 0.25f;
-            const SimdVector alphax_sum = multiplyAdd(x1, half, x0); // alphax_sum, alpha2_sum
-            const SimdVector alpha2_sum = alphax_sum.splatW();
-
-            //const Vector3 betax_sum = x2 + x1 * 0.5f;
-            //const float beta2_sum = w2 + w1 * 0.25f;
-            const SimdVector betax_sum = multiplyAdd(x1, half, x2); // betax_sum, beta2_sum
-            const SimdVector beta2_sum = betax_sum.splatW();
-
-            //const float alphabeta_sum = w1 * 0.25f;
-            const SimdVector alphabeta_sum = (x1 * half).splatW(); // alphabeta_sum
-
-            // const float factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
-            const SimdVector factor = reciprocal( negativeMultiplySubtract(alphabeta_sum, alphabeta_sum, alpha2_sum*beta2_sum) );
-
-            SimdVector a = negativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum*beta2_sum) * factor;
-            SimdVector b = negativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum*alpha2_sum) * factor;
-
-            // clamp to the grid
-            a = min( one, max( zero, a ) );
-            b = min( one, max( zero, b ) );
-            a = truncate( multiplyAdd( grid, a, half ) ) * gridrcp;
-            b = truncate( multiplyAdd( grid, b, half ) ) * gridrcp;
-
-            // compute the error (we skip the constant xxsum)
-            SimdVector e1 = multiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
-            SimdVector e2 = negativeMultiplySubtract( a, alphax_sum, a*b*alphabeta_sum );
-            SimdVector e3 = negativeMultiplySubtract( b, betax_sum, e2 );
-            SimdVector e4 = multiplyAdd( two, e3, e1 );
-
-            // apply the metric to the error term
-            SimdVector e5 = e4 * m_metricSqr;
-            SimdVector error = e5.splatX() + e5.splatY() + e5.splatZ();
-
-            // keep the solution if it wins
-            if (compareAnyLessThan(error, besterror))
-            {
-                besterror = error;
-                beststart = a;
-                bestend = b;
-            }
-
-            x1 += m_weighted[c0+c1];
-        }
-
-        x0 += m_weighted[c0];
-    }
-
-    // save the block if necessary
-    if (compareAnyLessThan(besterror, m_besterror))
-    {
-        *start = beststart.toVector3();
-        *end = bestend.toVector3();
-
-        // save the error
-        m_besterror = besterror;
-
-        return true;
-    }
-
-    return false;
-}
-
-bool ClusterFit::compress4( Vector3 * start, Vector3 * end )
-{
-    const int count = m_count;
-    const SimdVector one = SimdVector(1.0f);
-    const SimdVector zero = SimdVector(0.0f);
-    const SimdVector half = SimdVector(0.5f);
-    const SimdVector two = SimdVector(2.0);
-    const SimdVector onethird( 1.0f/3.0f, 1.0f/3.0f, 1.0f/3.0f, 1.0f/9.0f );
-    const SimdVector twothirds( 2.0f/3.0f, 2.0f/3.0f, 2.0f/3.0f, 4.0f/9.0f );
-    const SimdVector twonineths = SimdVector( 2.0f/9.0f );
-    const SimdVector grid( 31.0f, 63.0f, 31.0f, 0.0f );
-    const SimdVector gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f, 0.0f );
-
-    // declare variables
-    SimdVector beststart = SimdVector( 0.0f );
-    SimdVector bestend = SimdVector( 0.0f );
-    SimdVector besterror = SimdVector( FLT_MAX );
-
-    SimdVector x0 = zero;
-
-    // check all possible clusters for this total order
-    for (int c0 = 0; c0 <= count; c0++)
-    {
-        SimdVector x1 = zero;
-
-        for (int c1 = 0; c1 <= count-c0; c1++)
-        {
-            SimdVector x2 = zero;
-
-            for (int c2 = 0; c2 <= count-c0-c1; c2++)
-            {
-                const SimdVector x3 = m_xsum - x2 - x1 - x0;
-
-                //const Vector3 alphax_sum = x0 + x1 * (2.0f / 3.0f) + x2 * (1.0f / 3.0f);
-                //const float alpha2_sum = w0 + w1 * (4.0f/9.0f) + w2 * (1.0f/9.0f);
-                const SimdVector alphax_sum = multiplyAdd(x2, onethird, multiplyAdd(x1, twothirds, x0)); // alphax_sum, alpha2_sum
-                const SimdVector alpha2_sum = alphax_sum.splatW();
-
-                //const Vector3 betax_sum = x3 + x2 * (2.0f / 3.0f) + x1 * (1.0f / 3.0f);
-                //const float beta2_sum = w3 + w2 * (4.0f/9.0f) + w1 * (1.0f/9.0f);
-                const SimdVector betax_sum = multiplyAdd(x2, twothirds, multiplyAdd(x1, onethird, x3)); // betax_sum, beta2_sum
-                const SimdVector beta2_sum = betax_sum.splatW();
-
-                //const float alphabeta_sum = (w1 + w2) * (2.0f/9.0f);
-                const SimdVector alphabeta_sum = twonineths*( x1 + x2 ).splatW(); // alphabeta_sum
-
-                //const float factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
-                const SimdVector factor = reciprocal( negativeMultiplySubtract(alphabeta_sum, alphabeta_sum, alpha2_sum*beta2_sum) );
-
-                SimdVector a = negativeMultiplySubtract(betax_sum, alphabeta_sum, alphax_sum*beta2_sum) * factor;
-                SimdVector b = negativeMultiplySubtract(alphax_sum, alphabeta_sum, betax_sum*alpha2_sum) * factor;
-
-                // clamp to the grid
-                a = min( one, max( zero, a ) );
-                b = min( one, max( zero, b ) );
-                a = truncate( multiplyAdd( grid, a, half ) ) * gridrcp;
-                b = truncate( multiplyAdd( grid, b, half ) ) * gridrcp;
-
-                // compute the error (we skip the constant xxsum)
-                // error = a*a*alpha2_sum + b*b*beta2_sum + 2.0f*( a*b*alphabeta_sum - a*alphax_sum - b*betax_sum );
-                SimdVector e1 = multiplyAdd( a*a, alpha2_sum, b*b*beta2_sum );
-                SimdVector e2 = negativeMultiplySubtract( a, alphax_sum, a*b*alphabeta_sum );
-                SimdVector e3 = negativeMultiplySubtract( b, betax_sum, e2 );
-                SimdVector e4 = multiplyAdd( two, e3, e1 );
-
-                // apply the metric to the error term
-                SimdVector e5 = e4 * m_metricSqr;
-                SimdVector error = e5.splatX() + e5.splatY() + e5.splatZ();
-
-                // keep the solution if it wins
-                if (compareAnyLessThan(error, besterror))
-                {
-                    besterror = error;
-                    beststart = a;
-                    bestend = b;
-                }
-
-                x2 += m_weighted[c0+c1+c2];
-            }
-
-            x1 += m_weighted[c0+c1];
-        }
-
-        x0 += m_weighted[c0];
-    }
-
-    // save the block if necessary
-    if (compareAnyLessThan(besterror, m_besterror))
-    {
-        *start = beststart.toVector3();
-        *end = bestend.toVector3();
-
-        // save the error
-        m_besterror = besterror;
-
-        return true;
-    }
-
-    return false;
-}
-
-#else
-
-static const float midpoints5[32] = {
-    0.015686f, 0.047059f, 0.078431f, 0.111765f, 0.145098f, 0.176471f, 0.207843f, 0.241176f, 0.274510f, 0.305882f, 0.337255f, 0.370588f, 0.403922f, 0.435294f, 0.466667f, 0.5f,
-    0.533333f, 0.564706f, 0.596078f, 0.629412f, 0.662745f, 0.694118f, 0.725490f, 0.758824f, 0.792157f, 0.823529f, 0.854902f, 0.888235f, 0.921569f, 0.952941f, 0.984314f, 1.0f
-};
-
-static const float midpoints6[64] = {
-    0.007843f, 0.023529f, 0.039216f, 0.054902f, 0.070588f, 0.086275f, 0.101961f, 0.117647f, 0.133333f, 0.149020f, 0.164706f, 0.180392f, 0.196078f, 0.211765f, 0.227451f, 0.245098f,
-    0.262745f, 0.278431f, 0.294118f, 0.309804f, 0.325490f, 0.341176f, 0.356863f, 0.372549f, 0.388235f, 0.403922f, 0.419608f, 0.435294f, 0.450980f, 0.466667f, 0.482353f, 0.500000f,
-    0.517647f, 0.533333f, 0.549020f, 0.564706f, 0.580392f, 0.596078f, 0.611765f, 0.627451f, 0.643137f, 0.658824f, 0.674510f, 0.690196f, 0.705882f, 0.721569f, 0.737255f, 0.754902f,
-    0.772549f, 0.788235f, 0.803922f, 0.819608f, 0.835294f, 0.850980f, 0.866667f, 0.882353f, 0.898039f, 0.913725f, 0.929412f, 0.945098f, 0.960784f, 0.976471f, 0.992157f, 1.0f
-};
-
-// This is the ideal way to round, but it's too expensive to do this in the inner loop.
-inline Vector3 round565(const Vector3 & v) {
-    const Vector3 grid(31.0f, 63.0f, 31.0f);
-    const Vector3 gridrcp(1.0f / 31.0f, 1.0f / 63.0f, 1.0f / 31.0f);
-
-    Vector3 q = floor(grid * v);
-    q.x += (v.x > midpoints5[int(q.x)]);
-    q.y += (v.y > midpoints6[int(q.y)]);
-    q.z += (v.z > midpoints5[int(q.z)]);
-    q *= gridrcp;
-    return q;
-}
-
-bool ClusterFit::compress3(Vector3 * start, Vector3 * end)
-{
-    const uint count = m_count;
-    const Vector3 grid( 31.0f, 63.0f, 31.0f );
-    const Vector3 gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f );
-
-    // declare variables
-    Vector3 beststart( 0.0f );
-    Vector3 bestend( 0.0f );
-    float besterror = FLT_MAX;
-
-    Vector3 x0(0.0f);
-    float w0 = 0.0f;
-
-    int b0 = 0, b1 = 0;
-
-    // check all possible clusters for this total order
-    for (uint c0 = 0; c0 <= count; c0++)
-    {
-        Vector3 x1(0.0f);
-        float w1 = 0.0f;
-
-        for (uint c1 = 0; c1 <= count-c0; c1++)
-        {
-            float w2 = m_wsum - w0 - w1;
-
-            // These factors could be entirely precomputed.
-            float const alpha2_sum = w0 + w1 * 0.25f;
-            float const beta2_sum = w2 + w1 * 0.25f;
-            float const alphabeta_sum = w1 * 0.25f;
-            float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
-
-            Vector3 const alphax_sum = x0 + x1 * 0.5f;
-            Vector3 const betax_sum = m_xsum - alphax_sum;
-
-            Vector3 a = (alphax_sum*beta2_sum - betax_sum*alphabeta_sum) * factor;
-            Vector3 b = (betax_sum*alpha2_sum - alphax_sum*alphabeta_sum) * factor;
-
-            // clamp to the grid
-            a = clamp(a, 0, 1);
-            b = clamp(b, 0, 1);
-#if 1
-            a = floor(grid * a + 0.5f) * gridrcp;
-            b = floor(grid * b + 0.5f) * gridrcp;
-#else
-            a = round565(a);
-            b = round565(b);
-#endif
-
-            // compute the error
-            Vector3 e1 = a*a*alpha2_sum + b*b*beta2_sum + 2.0f*( a*b*alphabeta_sum - a*alphax_sum - b*betax_sum );
-
-            // apply the metric to the error term
-            float error = dot(e1, m_metricSqr);
-
-            // keep the solution if it wins
-            if (error < besterror)
-            {
-                besterror = error;
-                beststart = a;
-                bestend = b;
-                b0 = c0;
-                b1 = c1;
-            }
-
-            x1 += m_weighted[c0+c1];
-            w1 += m_weights[c0+c1];
-        }
-
-        x0 += m_weighted[c0];
-        w0 += m_weights[c0];
-    }
-
-    // save the block if necessary
-    if (besterror < m_besterror)
-    {
-
-        *start = beststart;
-        *end = bestend;
-
-        // save the error
-        m_besterror = besterror;
-
-        return true;
-    }
-
-    return false;
-}
-
-bool ClusterFit::compress4(Vector3 * start, Vector3 * end)
-{
-    const uint count = m_count;
-    const Vector3 grid( 31.0f, 63.0f, 31.0f );
-    const Vector3 gridrcp( 1.0f/31.0f, 1.0f/63.0f, 1.0f/31.0f );
-
-    // declare variables
-    Vector3 beststart( 0.0f );
-    Vector3 bestend( 0.0f );
-    float besterror = FLT_MAX;
-
-    Vector3 x0(0.0f);
-    float w0 = 0.0f;
-    int b0 = 0, b1 = 0, b2 = 0;
-
-    // check all possible clusters for this total order
-    for (uint c0 = 0; c0 <= count; c0++)
-    {
-        Vector3 x1(0.0f);
-        float w1 = 0.0f;
-
-        for (uint c1 = 0; c1 <= count-c0; c1++)
-        {
-            Vector3 x2(0.0f);
-            float w2 = 0.0f;
-
-            for (uint c2 = 0; c2 <= count-c0-c1; c2++)
-            {
-                float w3 = m_wsum - w0 - w1 - w2;
-
-                float const alpha2_sum = w0 + w1 * (4.0f/9.0f) + w2 * (1.0f/9.0f);
-                float const beta2_sum = w3 + w2 * (4.0f/9.0f) + w1 * (1.0f/9.0f);
-                float const alphabeta_sum = (w1 + w2) * (2.0f/9.0f);
-                float const factor = 1.0f / (alpha2_sum * beta2_sum - alphabeta_sum * alphabeta_sum);
-
-                Vector3 const alphax_sum = x0 + x1 * (2.0f / 3.0f) + x2 * (1.0f / 3.0f);
-                Vector3 const betax_sum = m_xsum - alphax_sum;
-
-                Vector3 a = ( alphax_sum*beta2_sum - betax_sum*alphabeta_sum )*factor;
-                Vector3 b = ( betax_sum*alpha2_sum - alphax_sum*alphabeta_sum )*factor;
-
-                // clamp to the grid
-                a = clamp(a, 0, 1);
-                b = clamp(b, 0, 1);
-#if 1
-                a = floor(a * grid + 0.5f) * gridrcp;
-                b = floor(b * grid + 0.5f) * gridrcp;
-#else
-                a = round565(a);
-                b = round565(b);
-#endif
-                // @@ It would be much more accurate to evaluate the error exactly. 
-
-                // compute the error
-                Vector3 e1 = a*a*alpha2_sum + b*b*beta2_sum + 2.0f*( a*b*alphabeta_sum - a*alphax_sum - b*betax_sum );
-
-                // apply the metric to the error term
-                float error = dot( e1, m_metricSqr );
-
-                // keep the solution if it wins
-                if (error < besterror)
-                {
-                    besterror = error;
-                    beststart = a;
-                    bestend = b;
-                    b0 = c0;
-                    b1 = c1;
-                    b2 = c2;
-                }
-
-                x2 += m_weighted[c0+c1+c2];
-                w2 += m_weights[c0+c1+c2];
-            }
-
-            x1 += m_weighted[c0+c1];
-            w1 += m_weights[c0+c1];
-        }
-
-        x0 += m_weighted[c0];
-        w0 += m_weights[c0];
-    }
-
-    // save the block if necessary
-    if (besterror < m_besterror)
-    {
-        *start = beststart;
-        *end = bestend;
-
-        // save the error
-        m_besterror = besterror;
-
-        return true;
-    }
-
-    return false;
-}
-
-#endif // NVTT_USE_SIMD
-
-//  Copyright (c) 2006-2020 Ignacio Castano                 icastano@nvidia.com
-//  Copyright (c) 2006 Simon Brown                          si@sjbrown.co.uk
-//
-//  Permission is hereby granted, free of charge, to any person obtaining
-//  a copy of this software and associated documentation files (the
-//  "Software"), to	deal in the Software without restriction, including
-//  without limitation the rights to use, copy, modify, merge, publish,
-//  distribute, sublicense, and/or sell copies of the Software, and to
-//  permit persons to whom the Software is furnished to do so, subject to
-//  the following conditions:
-//
-//  The above copyright notice and this permission notice shall be included
-//  in all copies or substantial portions of the Software.
-//
-//  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
-//  OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-//  MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-//  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-//  CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
-//  TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-//  SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/src/nvtt/ClusterFit.h b/src/nvtt/ClusterFit.h
deleted file mode 100644
index 9a15f33..0000000
--- a/src/nvtt/ClusterFit.h
+++ /dev/null
@@ -1,86 +0,0 @@
-// MIT license see full LICENSE text at end of file
-#pragma once
-
-#include "nvmath/SimdVector.h"
-#include "nvmath/Vector.h"
-
-// Use SIMD version if altivec or SSE are available.
-#define NVTT_USE_SIMD (NV_USE_ALTIVEC || NV_USE_SSE)
-//#define NVTT_USE_SIMD 0
-
-#include <xmmintrin.h>
-#if (NV_USE_SSE > 1)
-#include <emmintrin.h>
-#endif
-
-#ifndef NV_ALIGN_16
-#if NV_CC_GNUC
-#   define NV_ALIGN_16 __attribute__ ((__aligned__ (16)))
-#else
-#   define NV_ALIGN_16 __declspec(align(16))
-#endif
-#endif
-
-namespace nv {
-
-    class ClusterFit
-    {
-    public:
-        ClusterFit() {}
-
-        void setColorSet(const Vector3 * colors, const float * weights, int count);
-
-        void setColorWeights(const Vector4 & w);
-        float bestError() const;
-
-        bool compress3(Vector3 * start, Vector3 * end);
-        bool compress4(Vector3 * start, Vector3 * end);
-
-    private:
-
-        uint m_count;
-
-        // IC: Color and weight arrays are larger than necessary to avoid compiler warning.
-
-    #if NVTT_USE_SIMD
-        NV_ALIGN_16 SimdVector m_weighted[17];  // color | weight
-        SimdVector m_metric;                    // vec3
-        SimdVector m_metricSqr;                 // vec3
-        SimdVector m_xxsum;                     // color | weight
-        SimdVector m_xsum;                      // color | weight (wsum)
-        SimdVector m_besterror;                 // scalar
-    #else
-        Vector3 m_weighted[17];
-        float m_weights[17];
-        Vector3 m_metric;
-        Vector3 m_metricSqr;
-        Vector3 m_xxsum;
-        Vector3 m_xsum;
-        float m_wsum;
-        float m_besterror;
-    #endif
-    };
-
-} // nv namespace
-
-//  Copyright (c) 2006-2020 Ignacio Castano                 icastano@nvidia.com
-//  Copyright (c) 2006 Simon Brown                          si@sjbrown.co.uk
-//
-//  Permission is hereby granted, free of charge, to any person obtaining
-//  a copy of this software and associated documentation files (the
-//  "Software"), to	deal in the Software without restriction, including
-//  without limitation the rights to use, copy, modify, merge, publish,
-//  distribute, sublicense, and/or sell copies of the Software, and to
-//  permit persons to whom the Software is furnished to do so, subject to
-//  the following conditions:
-//
-//  The above copyright notice and this permission notice shall be included
-//  in all copies or substantial portions of the Software.
-//
-//  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
-//  OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-//  MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-//  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-//  CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
-//  TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-//  SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.