forked from cbuchner1/CudaMiner
-
Notifications
You must be signed in to change notification settings - Fork 0
/
nv_kernel2.cu
645 lines (539 loc) · 25.5 KB
/
nv_kernel2.cu
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
//
// Experimental Kernel for Kepler (Compute 3.5) devices
// code submitted by nVidia performance engineer Alexey Panteleev
// with modifications by Christian Buchner
//
// for Compute 3.5
// NOTE: compile this .cu module for compute_35,sm_35 with --maxrregcount=80
// for Compute 3.0
// NOTE: compile this .cu module for compute_30,sm_30 with --maxrregcount=64
//
#ifdef WIN32
#include <windows.h>
#endif
#include <stdio.h>
#include <time.h>
#include <sys/time.h>
#include <unistd.h>
#include <cuda.h>
#include "miner.h"
#include "nv_kernel2.h"
#define THREADS_PER_WU 1 // single thread per hash
#define TEXWIDTH 32768
#if __CUDA_ARCH__ < 350
// Kepler (Compute 3.0)
#define __ldg(x) (*(x))
#endif
// grab lane ID
static __device__ __inline__ unsigned int __laneId() { unsigned int laneId; asm( "mov.u32 %0, %%laneid;" : "=r"( laneId ) ); return laneId; }
// forward references
template <int ALGO> __global__ void nv2_scrypt_core_kernelA(uint32_t *g_idata, int begin, int end);
template <int ALGO, int TEX_DIM> __global__ void nv2_scrypt_core_kernelB(uint32_t *g_odata, int begin, int end);
// scratchbuf constants (pointers to scratch buffer for each work unit)
__constant__ uint32_t* c_V[TOTAL_WARP_LIMIT];
// using texture references for the "tex" variants of the B kernels
texture<uint4, 1, cudaReadModeElementType> texRef1D_4_V;
texture<uint4, 2, cudaReadModeElementType> texRef2D_4_V;
// iteration count N
__constant__ uint32_t c_N;
NV2Kernel::NV2Kernel() : KernelInterface()
{
}
bool NV2Kernel::bindtexture_1D(uint32_t *d_V, size_t size)
{
cudaChannelFormatDesc channelDesc4 = cudaCreateChannelDesc<uint4>();
texRef1D_4_V.normalized = 0;
texRef1D_4_V.filterMode = cudaFilterModePoint;
texRef1D_4_V.addressMode[0] = cudaAddressModeClamp;
checkCudaErrors(cudaBindTexture(NULL, &texRef1D_4_V, d_V, &channelDesc4, size));
return true;
}
bool NV2Kernel::bindtexture_2D(uint32_t *d_V, int width, int height, size_t pitch)
{
cudaChannelFormatDesc channelDesc4 = cudaCreateChannelDesc<uint4>();
texRef2D_4_V.normalized = 0;
texRef2D_4_V.filterMode = cudaFilterModePoint;
texRef2D_4_V.addressMode[0] = cudaAddressModeClamp;
texRef2D_4_V.addressMode[1] = cudaAddressModeClamp;
// maintain texture width of TEXWIDTH (max. limit is 65000)
while (width > TEXWIDTH) { width /= 2; height *= 2; pitch /= 2; }
while (width < TEXWIDTH) { width *= 2; height = (height+1)/2; pitch *= 2; }
checkCudaErrors(cudaBindTexture2D(NULL, &texRef2D_4_V, d_V, &channelDesc4, width, height, pitch));
return true;
}
bool NV2Kernel::unbindtexture_1D()
{
checkCudaErrors(cudaUnbindTexture(texRef1D_4_V));
return true;
}
bool NV2Kernel::unbindtexture_2D()
{
checkCudaErrors(cudaUnbindTexture(texRef2D_4_V));
return true;
}
void NV2Kernel::set_scratchbuf_constants(int MAXWARPS, uint32_t** h_V)
{
checkCudaErrors(cudaMemcpyToSymbol(c_V, h_V, MAXWARPS*sizeof(uint32_t*), 0, cudaMemcpyHostToDevice));
}
bool NV2Kernel::run_kernel(dim3 grid, dim3 threads, int WARPS_PER_BLOCK, int thr_id, cudaStream_t stream, uint32_t* d_idata, uint32_t* d_odata, unsigned int N, unsigned int LOOKUP_GAP, bool interactive, bool benchmark, int texture_cache)
{
bool success = true;
// clear CUDA's error variable
cudaGetLastError();
// make some constants available to kernel, update only initially and when changing
static int prev_N = 0;
if (N != prev_N) {
uint32_t h_N = N;
checkCudaErrors(cudaMemcpyToSymbolAsync(c_N, &h_N, sizeof(uint32_t), 0, cudaMemcpyHostToDevice, stream));
prev_N = N;
}
// First phase: Sequential writes to scratchpad.
const int batch = device_batchsize[thr_id];
const int sleeptime = 100;
unsigned int pos = 0;
int situation = 0;
do
{
switch(opt_algo) {
case ALGO_SCRYPT: nv2_scrypt_core_kernelA<ALGO_SCRYPT> <<< grid, threads, 0, stream >>>(d_idata, pos, min(pos+batch, N)); break;
case ALGO_SCRYPT_JANE: nv2_scrypt_core_kernelA<ALGO_SCRYPT_JANE><<< grid, threads, 0, stream >>>(d_idata, pos, min(pos+batch, N)); break;
}
if (!benchmark && interactive) {
checkCudaErrors(MyStreamSynchronize(stream, ++situation, thr_id));
usleep(sleeptime);
}
pos += batch;
} while (pos < N);
// Second phase: Random read access from scratchpad.
pos = 0;
do
{
if (pos > 0 && !benchmark && interactive) {
checkCudaErrors(MyStreamSynchronize(stream, ++situation, thr_id));
usleep(sleeptime);
}
if (texture_cache == 0) switch(opt_algo) {
case ALGO_SCRYPT: nv2_scrypt_core_kernelB<ALGO_SCRYPT ,0><<< grid, threads, 0, stream >>>(d_odata, pos, min(pos+batch, N)); break;
case ALGO_SCRYPT_JANE: nv2_scrypt_core_kernelB<ALGO_SCRYPT_JANE,0><<< grid, threads, 0, stream >>>(d_odata, pos, min(pos+batch, N)); break;
}
else if (texture_cache == 1) switch(opt_algo) {
case ALGO_SCRYPT: nv2_scrypt_core_kernelB<ALGO_SCRYPT ,1><<< grid, threads, 0, stream >>>(d_odata, pos, min(pos+batch, N)); break;
case ALGO_SCRYPT_JANE: nv2_scrypt_core_kernelB<ALGO_SCRYPT_JANE,1><<< grid, threads, 0, stream >>>(d_odata, pos, min(pos+batch, N)); break;
}
else if (texture_cache == 2) switch(opt_algo) {
case ALGO_SCRYPT: nv2_scrypt_core_kernelB<ALGO_SCRYPT ,2><<< grid, threads, 0, stream >>>(d_odata, pos, min(pos+batch, N)); break;
case ALGO_SCRYPT_JANE: nv2_scrypt_core_kernelB<ALGO_SCRYPT_JANE,2><<< grid, threads, 0, stream >>>(d_odata, pos, min(pos+batch, N)); break;
}
pos += batch;
} while (pos < N);
// catch any kernel launch failures
if (cudaPeekAtLastError() != cudaSuccess) success = false;
return success;
}
static __device__ uint4& operator^=(uint4& left, const uint4& right)
{
left.x ^= right.x;
left.y ^= right.y;
left.z ^= right.z;
left.w ^= right.w;
return left;
}
__device__ __forceinline__ uint4 __shfl(const uint4 val, unsigned int lane, unsigned int width)
{
return make_uint4(
(unsigned int)__shfl((int)val.x, lane, width),
(unsigned int)__shfl((int)val.y, lane, width),
(unsigned int)__shfl((int)val.z, lane, width),
(unsigned int)__shfl((int)val.w, lane, width));
}
__device__ __forceinline__ void __transposed_write_BC(uint4 (&B)[4], uint4 (&C)[4], uint4 *D, int spacing)
{
unsigned int laneId = __laneId();
unsigned int lane8 = laneId%8;
unsigned int tile = laneId/8;
uint4 T1[8], T2[8];
/* Source matrix, A-H are threads, 0-7 are data items, thread A is marked with `*`:
*A0 B0 C0 D0 E0 F0 G0 H0
*A1 B1 C1 D1 E1 F1 G1 H1
*A2 B2 C2 D2 E2 F2 G2 H2
*A3 B3 C3 D3 E3 F3 G3 H3
*A4 B4 C4 D4 E4 F4 G4 H4
*A5 B5 C5 D5 E5 F5 G5 H5
*A6 B6 C6 D6 E6 F6 G6 H6
*A7 B7 C7 D7 E7 F7 G7 H7
*/
// rotate rows
T1[0] = B[0];
T1[1] = __shfl(B[1], lane8 + 7, 8);
T1[2] = __shfl(B[2], lane8 + 6, 8);
T1[3] = __shfl(B[3], lane8 + 5, 8);
T1[4] = __shfl(C[0], lane8 + 4, 8);
T1[5] = __shfl(C[1], lane8 + 3, 8);
T1[6] = __shfl(C[2], lane8 + 2, 8);
T1[7] = __shfl(C[3], lane8 + 1, 8);
/* Matrix after row rotates:
*A0 B0 C0 D0 E0 F0 G0 H0
H1 *A1 B1 C1 D1 E1 F1 G1
G2 H2 *A2 B2 C2 D2 E2 F2
F3 G3 H3 *A3 B3 C3 D3 E3
E4 F4 G4 H4 *A4 B4 C4 D4
D5 E5 F5 G5 H5 *A5 B5 C5
C6 D6 E6 F6 G6 H6 *A6 B6
B7 C7 D7 E7 F7 G7 H7 *A7
*/
// rotate columns up using a barrel shifter simulation
// column X is rotated up by (X+1) items
#pragma unroll 8
for(int n = 0; n < 8; n++) T2[n] = ((lane8+1) & 1) ? T1[(n+1) % 8] : T1[n];
#pragma unroll 8
for(int n = 0; n < 8; n++) T1[n] = ((lane8+1) & 2) ? T2[(n+2) % 8] : T2[n];
#pragma unroll 8
for(int n = 0; n < 8; n++) T2[n] = ((lane8+1) & 4) ? T1[(n+4) % 8] : T1[n];
/* Matrix after column rotates:
H1 H2 H3 H4 H5 H6 H7 H0
G2 G3 G4 G5 G6 G7 G0 G1
F3 F4 F5 F6 F7 F0 F1 F2
E4 E5 E6 E7 E0 E1 E2 E3
D5 D6 D7 D0 D1 D2 D3 D4
C6 C7 C0 C1 C2 C3 C4 C5
B7 B0 B1 B2 B3 B4 B5 B6
*A0 *A1 *A2 *A3 *A4 *A5 *A6 *A7
*/
// rotate rows again using address math and write to D, in reverse row order
D[spacing*2*(32*tile )+ lane8 ] = T2[7];
D[spacing*2*(32*tile+4 )+(lane8+7)%8] = T2[6];
D[spacing*2*(32*tile+8 )+(lane8+6)%8] = T2[5];
D[spacing*2*(32*tile+12)+(lane8+5)%8] = T2[4];
D[spacing*2*(32*tile+16)+(lane8+4)%8] = T2[3];
D[spacing*2*(32*tile+20)+(lane8+3)%8] = T2[2];
D[spacing*2*(32*tile+24)+(lane8+2)%8] = T2[1];
D[spacing*2*(32*tile+28)+(lane8+1)%8] = T2[0];
}
template <int TEX_DIM> __device__ __forceinline__ void __transposed_read_BC(const uint4 *S, uint4 (&B)[4], uint4 (&C)[4], int spacing, int row)
{
unsigned int laneId = __laneId();
unsigned int lane8 = laneId%8;
unsigned int tile = laneId/8;
// Perform the same transposition as in __transposed_write_BC, but in reverse order.
// See the illustrations in comments for __transposed_write_BC.
// read and rotate rows, in reverse row order
uint4 T1[8], T2[8];
const uint4 *loc;
loc = &S[(spacing*2*(32*tile ) + lane8 + 8*__shfl(row, 0, 8))];
T1[7] = TEX_DIM==0 ? __ldg(loc) : TEX_DIM==1 ? tex1Dfetch(texRef1D_4_V, loc-(uint4*)c_V[0]) : tex2D(texRef2D_4_V, 0.5f + ((loc-(uint4*)c_V[0])%TEXWIDTH), 0.5f + ((loc-(uint4*)c_V[0])/TEXWIDTH));
loc = &S[(spacing*2*(32*tile+4 ) + (lane8+7)%8 + 8*__shfl(row, 1, 8))];
T1[6] = TEX_DIM==0 ? __ldg(loc) : TEX_DIM==1 ? tex1Dfetch(texRef1D_4_V, loc-(uint4*)c_V[0]) : tex2D(texRef2D_4_V, 0.5f + ((loc-(uint4*)c_V[0])%TEXWIDTH), 0.5f + ((loc-(uint4*)c_V[0])/TEXWIDTH));
loc = &S[(spacing*2*(32*tile+8 ) + (lane8+6)%8 + 8*__shfl(row, 2, 8))];
T1[5] = TEX_DIM==0 ? __ldg(loc) : TEX_DIM==1 ? tex1Dfetch(texRef1D_4_V, loc-(uint4*)c_V[0]) : tex2D(texRef2D_4_V, 0.5f + ((loc-(uint4*)c_V[0])%TEXWIDTH), 0.5f + ((loc-(uint4*)c_V[0])/TEXWIDTH));
loc = &S[(spacing*2*(32*tile+12) + (lane8+5)%8 + 8*__shfl(row, 3, 8))];
T1[4] = TEX_DIM==0 ? __ldg(loc) : TEX_DIM==1 ? tex1Dfetch(texRef1D_4_V, loc-(uint4*)c_V[0]) : tex2D(texRef2D_4_V, 0.5f + ((loc-(uint4*)c_V[0])%TEXWIDTH), 0.5f + ((loc-(uint4*)c_V[0])/TEXWIDTH));
loc = &S[(spacing*2*(32*tile+16) + (lane8+4)%8 + 8*__shfl(row, 4, 8))];
T1[3] = TEX_DIM==0 ? __ldg(loc) : TEX_DIM==1 ? tex1Dfetch(texRef1D_4_V, loc-(uint4*)c_V[0]) : tex2D(texRef2D_4_V, 0.5f + ((loc-(uint4*)c_V[0])%TEXWIDTH), 0.5f + ((loc-(uint4*)c_V[0])/TEXWIDTH));
loc = &S[(spacing*2*(32*tile+20) + (lane8+3)%8 + 8*__shfl(row, 5, 8))];
T1[2] = TEX_DIM==0 ? __ldg(loc) : TEX_DIM==1 ? tex1Dfetch(texRef1D_4_V, loc-(uint4*)c_V[0]) : tex2D(texRef2D_4_V, 0.5f + ((loc-(uint4*)c_V[0])%TEXWIDTH), 0.5f + ((loc-(uint4*)c_V[0])/TEXWIDTH));
loc = &S[(spacing*2*(32*tile+24) + (lane8+2)%8 + 8*__shfl(row, 6, 8))];
T1[1] = TEX_DIM==0 ? __ldg(loc) : TEX_DIM==1 ? tex1Dfetch(texRef1D_4_V, loc-(uint4*)c_V[0]) : tex2D(texRef2D_4_V, 0.5f + ((loc-(uint4*)c_V[0])%TEXWIDTH), 0.5f + ((loc-(uint4*)c_V[0])/TEXWIDTH));
loc = &S[(spacing*2*(32*tile+28) + (lane8+1)%8 + 8*__shfl(row, 7, 8))];
T1[0] = TEX_DIM==0 ? __ldg(loc) : TEX_DIM==1 ? tex1Dfetch(texRef1D_4_V, loc-(uint4*)c_V[0]) : tex2D(texRef2D_4_V, 0.5f + ((loc-(uint4*)c_V[0])%TEXWIDTH), 0.5f + ((loc-(uint4*)c_V[0])/TEXWIDTH));
// rotate columns down using a barrel shifter simulation
// column X is rotated down by (X+1) items, or up by (8-(X+1)) = (7-X) items
#pragma unroll 8
for(int n = 0; n < 8; n++) T2[n] = ((7-lane8) & 1) ? T1[(n+1) % 8] : T1[n];
#pragma unroll 8
for(int n = 0; n < 8; n++) T1[n] = ((7-lane8) & 2) ? T2[(n+2) % 8] : T2[n];
#pragma unroll 8
for(int n = 0; n < 8; n++) T2[n] = ((7-lane8) & 4) ? T1[(n+4) % 8] : T1[n];
// rotate rows
B[0] = T2[0];
B[1] = __shfl(T2[1], lane8 + 1, 8);
B[2] = __shfl(T2[2], lane8 + 2, 8);
B[3] = __shfl(T2[3], lane8 + 3, 8);
C[0] = __shfl(T2[4], lane8 + 4, 8);
C[1] = __shfl(T2[5], lane8 + 5, 8);
C[2] = __shfl(T2[6], lane8 + 6, 8);
C[3] = __shfl(T2[7], lane8 + 7, 8);
}
template <int TEX_DIM> __device__ __forceinline__ void __transposed_xor_BC(const uint4 *S, uint4 (&B)[4], uint4 (&C)[4], int spacing, int row)
{
uint4 BT[4], CT[4];
__transposed_read_BC<TEX_DIM>(S, BT, CT, spacing, row);
#pragma unroll 4
for(int n = 0; n < 4; n++)
{
B[n] ^= BT[n];
C[n] ^= CT[n];
}
}
#if __CUDA_ARCH__ < 350
// Kepler (Compute 3.0)
#define ROTL(a, b) ((a)<<(b))|((a)>>(32-(b)))
#else
// Kepler (Compute 3.5)
#define ROTL(a, b) __funnelshift_l( a, a, b );
#endif
#if 0
#define QUARTER(a,b,c,d) \
a += b; d ^= a; d = ROTL(d,16); \
c += d; b ^= c; b = ROTL(b,12); \
a += b; d ^= a; d = ROTL(d,8); \
c += d; b ^= c; b = ROTL(b,7);
static __device__ void xor_chacha8(uint4 *B, uint4 *C)
{
uint32_t x[16];
x[0]=(B[0].x ^= C[0].x);
x[1]=(B[0].y ^= C[0].y);
x[2]=(B[0].z ^= C[0].z);
x[3]=(B[0].w ^= C[0].w);
x[4]=(B[1].x ^= C[1].x);
x[5]=(B[1].y ^= C[1].y);
x[6]=(B[1].z ^= C[1].z);
x[7]=(B[1].w ^= C[1].w);
x[8]=(B[2].x ^= C[2].x);
x[9]=(B[2].y ^= C[2].y);
x[10]=(B[2].z ^= C[2].z);
x[11]=(B[2].w ^= C[2].w);
x[12]=(B[3].x ^= C[3].x);
x[13]=(B[3].y ^= C[3].y);
x[14]=(B[3].z ^= C[3].z);
x[15]=(B[3].w ^= C[3].w);
/* Operate on columns. */
QUARTER( x[0], x[4], x[ 8], x[12] )
QUARTER( x[1], x[5], x[ 9], x[13] )
QUARTER( x[2], x[6], x[10], x[14] )
QUARTER( x[3], x[7], x[11], x[15] )
/* Operate on diagonals */
QUARTER( x[0], x[5], x[10], x[15] )
QUARTER( x[1], x[6], x[11], x[12] )
QUARTER( x[2], x[7], x[ 8], x[13] )
QUARTER( x[3], x[4], x[ 9], x[14] )
/* Operate on columns. */
QUARTER( x[0], x[4], x[ 8], x[12] )
QUARTER( x[1], x[5], x[ 9], x[13] )
QUARTER( x[2], x[6], x[10], x[14] )
QUARTER( x[3], x[7], x[11], x[15] )
/* Operate on diagonals */
QUARTER( x[0], x[5], x[10], x[15] )
QUARTER( x[1], x[6], x[11], x[12] )
QUARTER( x[2], x[7], x[ 8], x[13] )
QUARTER( x[3], x[4], x[ 9], x[14] )
/* Operate on columns. */
QUARTER( x[0], x[4], x[ 8], x[12] )
QUARTER( x[1], x[5], x[ 9], x[13] )
QUARTER( x[2], x[6], x[10], x[14] )
QUARTER( x[3], x[7], x[11], x[15] )
/* Operate on diagonals */
QUARTER( x[0], x[5], x[10], x[15] )
QUARTER( x[1], x[6], x[11], x[12] )
QUARTER( x[2], x[7], x[ 8], x[13] )
QUARTER( x[3], x[4], x[ 9], x[14] )
/* Operate on columns. */
QUARTER( x[0], x[4], x[ 8], x[12] )
QUARTER( x[1], x[5], x[ 9], x[13] )
QUARTER( x[2], x[6], x[10], x[14] )
QUARTER( x[3], x[7], x[11], x[15] )
/* Operate on diagonals */
QUARTER( x[0], x[5], x[10], x[15] )
QUARTER( x[1], x[6], x[11], x[12] )
QUARTER( x[2], x[7], x[ 8], x[13] )
QUARTER( x[3], x[4], x[ 9], x[14] )
B[0].x += x[0]; B[0].y += x[1]; B[0].z += x[2]; B[0].w += x[3]; B[1].x += x[4]; B[1].y += x[5]; B[1].z += x[6]; B[1].w += x[7];
B[2].x += x[8]; B[2].y += x[9]; B[2].z += x[10]; B[2].w += x[11]; B[3].x += x[12]; B[3].y += x[13]; B[3].z += x[14]; B[3].w += x[15];
}
#else
#define ADD4(d1,d2,d3,d4,s1,s2,s3,s4) \
d1 += s1; d2 += s2; d3 += s3; d4 += s4;
#define XOR4(d1,d2,d3,d4,s1,s2,s3,s4) \
d1 ^= s1; d2 ^= s2; d3 ^= s3; d4 ^= s4;
#define ROTL4(d1,d2,d3,d4,amt) \
d1 = ROTL(d1, amt); d2 = ROTL(d2, amt); d3 = ROTL(d3, amt); d4 = ROTL(d4, amt);
#define QROUND(a1,a2,a3,a4, b1,b2,b3,b4, c1,c2,c3,c4, amt) \
ADD4 (a1,a2,a3,a4, c1,c2,c3,c4) \
XOR4 (b1,b2,b3,b4, a1,a2,a3,a4) \
ROTL4(b1,b2,b3,b4, amt)
static __device__ void xor_chacha8(uint4 *B, uint4 *C)
{
uint32_t x[16];
x[0]=(B[0].x ^= C[0].x);
x[1]=(B[0].y ^= C[0].y);
x[2]=(B[0].z ^= C[0].z);
x[3]=(B[0].w ^= C[0].w);
x[4]=(B[1].x ^= C[1].x);
x[5]=(B[1].y ^= C[1].y);
x[6]=(B[1].z ^= C[1].z);
x[7]=(B[1].w ^= C[1].w);
x[8]=(B[2].x ^= C[2].x);
x[9]=(B[2].y ^= C[2].y);
x[10]=(B[2].z ^= C[2].z);
x[11]=(B[2].w ^= C[2].w);
x[12]=(B[3].x ^= C[3].x);
x[13]=(B[3].y ^= C[3].y);
x[14]=(B[3].z ^= C[3].z);
x[15]=(B[3].w ^= C[3].w);
/* Operate on columns. */
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[12],x[13],x[14],x[15], x[ 4],x[ 5],x[ 6],x[ 7], 16);
QROUND(x[ 8],x[ 9],x[10],x[11], x[ 4],x[ 5],x[ 6],x[ 7], x[12],x[13],x[14],x[15], 12);
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[12],x[13],x[14],x[15], x[ 4],x[ 5],x[ 6],x[ 7], 8);
QROUND(x[ 8],x[ 9],x[10],x[11], x[ 4],x[ 5],x[ 6],x[ 7], x[12],x[13],x[14],x[15], 7);
/* Operate on diagonals */
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[15],x[12],x[13],x[14], x[ 5],x[ 6],x[ 7],x[ 4], 16);
QROUND(x[10],x[11],x[ 8],x[ 9], x[ 5],x[ 6],x[ 7],x[ 4], x[15],x[12],x[13],x[14], 12);
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[15],x[12],x[13],x[14], x[ 5],x[ 6],x[ 7],x[ 4], 8);
QROUND(x[10],x[11],x[ 8],x[ 9], x[ 5],x[ 6],x[ 7],x[ 4], x[15],x[12],x[13],x[14], 7);
/* Operate on columns. */
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[12],x[13],x[14],x[15], x[ 4],x[ 5],x[ 6],x[ 7], 16);
QROUND(x[ 8],x[ 9],x[10],x[11], x[ 4],x[ 5],x[ 6],x[ 7], x[12],x[13],x[14],x[15], 12);
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[12],x[13],x[14],x[15], x[ 4],x[ 5],x[ 6],x[ 7], 8);
QROUND(x[ 8],x[ 9],x[10],x[11], x[ 4],x[ 5],x[ 6],x[ 7], x[12],x[13],x[14],x[15], 7);
/* Operate on diagonals */
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[15],x[12],x[13],x[14], x[ 5],x[ 6],x[ 7],x[ 4], 16);
QROUND(x[10],x[11],x[ 8],x[ 9], x[ 5],x[ 6],x[ 7],x[ 4], x[15],x[12],x[13],x[14], 12);
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[15],x[12],x[13],x[14], x[ 5],x[ 6],x[ 7],x[ 4], 8);
QROUND(x[10],x[11],x[ 8],x[ 9], x[ 5],x[ 6],x[ 7],x[ 4], x[15],x[12],x[13],x[14], 7);
/* Operate on columns. */
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[12],x[13],x[14],x[15], x[ 4],x[ 5],x[ 6],x[ 7], 16);
QROUND(x[ 8],x[ 9],x[10],x[11], x[ 4],x[ 5],x[ 6],x[ 7], x[12],x[13],x[14],x[15], 12);
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[12],x[13],x[14],x[15], x[ 4],x[ 5],x[ 6],x[ 7], 8);
QROUND(x[ 8],x[ 9],x[10],x[11], x[ 4],x[ 5],x[ 6],x[ 7], x[12],x[13],x[14],x[15], 7);
/* Operate on diagonals */
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[15],x[12],x[13],x[14], x[ 5],x[ 6],x[ 7],x[ 4], 16);
QROUND(x[10],x[11],x[ 8],x[ 9], x[ 5],x[ 6],x[ 7],x[ 4], x[15],x[12],x[13],x[14], 12);
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[15],x[12],x[13],x[14], x[ 5],x[ 6],x[ 7],x[ 4], 8);
QROUND(x[10],x[11],x[ 8],x[ 9], x[ 5],x[ 6],x[ 7],x[ 4], x[15],x[12],x[13],x[14], 7);
/* Operate on columns. */
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[12],x[13],x[14],x[15], x[ 4],x[ 5],x[ 6],x[ 7], 16);
QROUND(x[ 8],x[ 9],x[10],x[11], x[ 4],x[ 5],x[ 6],x[ 7], x[12],x[13],x[14],x[15], 12);
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[12],x[13],x[14],x[15], x[ 4],x[ 5],x[ 6],x[ 7], 8);
QROUND(x[ 8],x[ 9],x[10],x[11], x[ 4],x[ 5],x[ 6],x[ 7], x[12],x[13],x[14],x[15], 7);
/* Operate on diagonals */
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[15],x[12],x[13],x[14], x[ 5],x[ 6],x[ 7],x[ 4], 16);
QROUND(x[10],x[11],x[ 8],x[ 9], x[ 5],x[ 6],x[ 7],x[ 4], x[15],x[12],x[13],x[14], 12);
QROUND(x[ 0],x[ 1],x[ 2],x[ 3], x[15],x[12],x[13],x[14], x[ 5],x[ 6],x[ 7],x[ 4], 8);
QROUND(x[10],x[11],x[ 8],x[ 9], x[ 5],x[ 6],x[ 7],x[ 4], x[15],x[12],x[13],x[14], 7);
B[0].x += x[0]; B[0].y += x[1]; B[0].z += x[2]; B[0].w += x[3]; B[1].x += x[4]; B[1].y += x[5]; B[1].z += x[6]; B[1].w += x[7];
B[2].x += x[8]; B[2].y += x[9]; B[2].z += x[10]; B[2].w += x[11]; B[3].x += x[12]; B[3].y += x[13]; B[3].z += x[14]; B[3].w += x[15];
}
#endif
#define ROTL7(a0,a1,a2,a3,a00,a10,a20,a30){\
a0^=ROTL(a00, 7); a1^=ROTL(a10, 7); a2^=ROTL(a20, 7); a3^=ROTL(a30, 7);\
};\
#define ROTL9(a0,a1,a2,a3,a00,a10,a20,a30){\
a0^=ROTL(a00, 9); a1^=ROTL(a10, 9); a2^=ROTL(a20, 9); a3^=ROTL(a30, 9);\
};\
#define ROTL13(a0,a1,a2,a3,a00,a10,a20,a30){\
a0^=ROTL(a00, 13); a1^=ROTL(a10, 13); a2^=ROTL(a20, 13); a3^=ROTL(a30, 13);\
};\
#define ROTL18(a0,a1,a2,a3,a00,a10,a20,a30){\
a0^=ROTL(a00, 18); a1^=ROTL(a10, 18); a2^=ROTL(a20, 18); a3^=ROTL(a30, 18);\
};\
static __device__ void xor_salsa8(uint4 *B, uint4 *C)
{
uint32_t x[16];
x[0]=(B[0].x ^= C[0].x);
x[1]=(B[0].y ^= C[0].y);
x[2]=(B[0].z ^= C[0].z);
x[3]=(B[0].w ^= C[0].w);
x[4]=(B[1].x ^= C[1].x);
x[5]=(B[1].y ^= C[1].y);
x[6]=(B[1].z ^= C[1].z);
x[7]=(B[1].w ^= C[1].w);
x[8]=(B[2].x ^= C[2].x);
x[9]=(B[2].y ^= C[2].y);
x[10]=(B[2].z ^= C[2].z);
x[11]=(B[2].w ^= C[2].w);
x[12]=(B[3].x ^= C[3].x);
x[13]=(B[3].y ^= C[3].y);
x[14]=(B[3].z ^= C[3].z);
x[15]=(B[3].w ^= C[3].w);
/* Operate on columns. */
ROTL7(x[4],x[9],x[14],x[3],x[0]+x[12],x[1]+x[5],x[6]+x[10],x[11]+x[15]);
ROTL9(x[8],x[13],x[2],x[7],x[0]+x[4],x[5]+x[9],x[10]+x[14],x[3]+x[15]);
ROTL13(x[12],x[1],x[6],x[11],x[4]+x[8],x[9]+x[13],x[2]+x[14],x[3]+x[7]);
ROTL18(x[0],x[5],x[10],x[15],x[8]+x[12],x[1]+x[13],x[2]+x[6],x[7]+x[11]);
/* Operate on rows. */
ROTL7(x[1],x[6],x[11],x[12],x[0]+x[3],x[4]+x[5],x[9]+x[10],x[14]+x[15]);
ROTL9(x[2],x[7],x[8],x[13],x[0]+x[1],x[5]+x[6],x[10]+x[11],x[12]+x[15]);
ROTL13(x[3],x[4],x[9],x[14],x[1]+x[2],x[6]+x[7],x[8]+x[11],x[12]+x[13]);
ROTL18(x[0],x[5],x[10],x[15],x[2]+x[3],x[4]+x[7],x[8]+x[9],x[13]+x[14]);
/* Operate on columns. */
ROTL7(x[4],x[9],x[14],x[3],x[0]+x[12],x[1]+x[5],x[6]+x[10],x[11]+x[15]);
ROTL9(x[8],x[13],x[2],x[7],x[0]+x[4],x[5]+x[9],x[10]+x[14],x[3]+x[15]);
ROTL13(x[12],x[1],x[6],x[11],x[4]+x[8],x[9]+x[13],x[2]+x[14],x[3]+x[7]);
ROTL18(x[0],x[5],x[10],x[15],x[8]+x[12],x[1]+x[13],x[2]+x[6],x[7]+x[11]);
/* Operate on rows. */
ROTL7(x[1],x[6],x[11],x[12],x[0]+x[3],x[4]+x[5],x[9]+x[10],x[14]+x[15]);
ROTL9(x[2],x[7],x[8],x[13],x[0]+x[1],x[5]+x[6],x[10]+x[11],x[12]+x[15]);
ROTL13(x[3],x[4],x[9],x[14],x[1]+x[2],x[6]+x[7],x[8]+x[11],x[12]+x[13]);
ROTL18(x[0],x[5],x[10],x[15],x[2]+x[3],x[4]+x[7],x[8]+x[9],x[13]+x[14]);
/* Operate on columns. */
ROTL7(x[4],x[9],x[14],x[3],x[0]+x[12],x[1]+x[5],x[6]+x[10],x[11]+x[15]);
ROTL9(x[8],x[13],x[2],x[7],x[0]+x[4],x[5]+x[9],x[10]+x[14],x[3]+x[15]);
ROTL13(x[12],x[1],x[6],x[11],x[4]+x[8],x[9]+x[13],x[2]+x[14],x[3]+x[7]);
ROTL18(x[0],x[5],x[10],x[15],x[8]+x[12],x[1]+x[13],x[2]+x[6],x[7]+x[11]);
/* Operate on rows. */
ROTL7(x[1],x[6],x[11],x[12],x[0]+x[3],x[4]+x[5],x[9]+x[10],x[14]+x[15]);
ROTL9(x[2],x[7],x[8],x[13],x[0]+x[1],x[5]+x[6],x[10]+x[11],x[12]+x[15]);
ROTL13(x[3],x[4],x[9],x[14],x[1]+x[2],x[6]+x[7],x[8]+x[11],x[12]+x[13]);
ROTL18(x[0],x[5],x[10],x[15],x[2]+x[3],x[4]+x[7],x[8]+x[9],x[13]+x[14]);
/* Operate on columns. */
ROTL7(x[4],x[9],x[14],x[3],x[0]+x[12],x[1]+x[5],x[6]+x[10],x[11]+x[15]);
ROTL9(x[8],x[13],x[2],x[7],x[0]+x[4],x[5]+x[9],x[10]+x[14],x[3]+x[15]);
ROTL13(x[12],x[1],x[6],x[11],x[4]+x[8],x[9]+x[13],x[2]+x[14],x[3]+x[7]);
ROTL18(x[0],x[5],x[10],x[15],x[8]+x[12],x[1]+x[13],x[2]+x[6],x[7]+x[11]);
/* Operate on rows. */
ROTL7(x[1],x[6],x[11],x[12],x[0]+x[3],x[4]+x[5],x[9]+x[10],x[14]+x[15]);
ROTL9(x[2],x[7],x[8],x[13],x[0]+x[1],x[5]+x[6],x[10]+x[11],x[12]+x[15]);
ROTL13(x[3],x[4],x[9],x[14],x[1]+x[2],x[6]+x[7],x[8]+x[11],x[12]+x[13]);
ROTL18(x[0],x[5],x[10],x[15],x[2]+x[3],x[4]+x[7],x[8]+x[9],x[13]+x[14]);
B[0].x += x[0]; B[0].y += x[1]; B[0].z += x[2]; B[0].w += x[3]; B[1].x += x[4]; B[1].y += x[5]; B[1].z += x[6]; B[1].w += x[7];
B[2].x += x[8]; B[2].y += x[9]; B[2].z += x[10]; B[2].w += x[11]; B[3].x += x[12]; B[3].y += x[13]; B[3].z += x[14]; B[3].w += x[15];
}
template <int ALGO> static __device__ void block_mixer(uint4 *B, uint4 *C)
{
switch (ALGO)
{
case ALGO_SCRYPT: xor_salsa8(B, C); break;
case ALGO_SCRYPT_JANE: xor_chacha8(B, C); break;
}
}
////////////////////////////////////////////////////////////////////////////////
//! Experimental Scrypt core kernel for Titan devices.
//! @param g_idata input data in global memory
//! @param g_odata output data in global memory
////////////////////////////////////////////////////////////////////////////////
template <int ALGO> __global__ void nv2_scrypt_core_kernelA(uint32_t *g_idata, int begin, int end)
{
int offset = blockIdx.x * blockDim.x + threadIdx.x / warpSize * warpSize;
g_idata += 32 * offset;
uint32_t * V = c_V[offset / warpSize];
uint4 B[4], C[4];
int i = begin;
if(i == 0) {
__transposed_read_BC<0>((uint4*)g_idata, B, C, 1, 0);
__transposed_write_BC(B, C, (uint4*)V, c_N);
++i;
} else
__transposed_read_BC<0>((uint4*)(V + (i-1)*32), B, C, c_N, 0);
while(i < end) {
block_mixer<ALGO>(B, C); block_mixer<ALGO>(C, B);
__transposed_write_BC(B, C, (uint4*)(V + i*32), c_N);
++i;
}
}
template <int ALGO, int TEX_DIM> __global__ void nv2_scrypt_core_kernelB(uint32_t *g_odata, int begin, int end)
{
int offset = blockIdx.x * blockDim.x + threadIdx.x / warpSize * warpSize;
g_odata += 32 * offset;
uint32_t * V = c_V[offset / warpSize];
uint4 B[4], C[4];
if(begin == 0) {
__transposed_read_BC<TEX_DIM>((uint4*)V, B, C, c_N, c_N - 1);
block_mixer<ALGO>(B, C); block_mixer<ALGO>(C, B);
} else
__transposed_read_BC<0>((uint4*)g_odata, B, C, 1, 0);
for (int i = begin; i < end; i++) {
int slot = C[0].x & (c_N - 1);
__transposed_xor_BC<TEX_DIM>((uint4*)(V), B, C, c_N, slot);
block_mixer<ALGO>(B, C); block_mixer<ALGO>(C, B);
}
__transposed_write_BC(B, C, (uint4*)(g_odata), 1);
}