GPU

Intel Graphics Driver v4501 for Windows

Published May 6, 2017

A new set of graphics driver is available for Intel 4th/5th/6th generation GPUs (Haswell, Broadwell and Skylake). This set is not very fresh (released on August 12, 2016) but is the latest available!

Many new features have added for 6th Gen Intel Core processor family (HD Graphics 520/530, Iris 540/550, Iris Pro 580):

– 6th Gen camera pipe Windows* 7 support
– 3 DVI/HDMI displays support on 6th Gen
– LACE: Local Adaptive Contrast Enhancement
– Unify color for different panels
– Support 5K3K Panels with content protection for both Overly and non-Overlay 15.36 drivers for 4th, 5th, 6th Gen and beyond
– User to select which outputs to make active when more display connections are available than can be driven by Intel graphics
– x2 DP mode support in display driver for type-C support

You can download this driver from THIS PAGE.

Intel v4501 - HD Graphics 530 - GPU Caps Viewer

Intel v4501 - HD Graphics 530 - GPU Caps Viewer - OpenCL panel

Intel v4501 is an OpenGL 4.4 and OpenCL 2.0 driver and exposes 226 OpenGL extensions (GL=205 and WGL=21) for a HD Graphics 530 GPU (Core i5 6600K). There is no Vulkan API support in v4501 ? If you need a Vulkan driver, try the v4404.

- OpenGL vendor: Intel
- OpenGL renderer: Intel(R) HD Graphics 530
- OpenGL Version: 4.4.0 - Build 20.19.15.4501
- GLSL (OpenGL Shading Language) Version: 4.40 - Build 20.19.15.4501

	- GL_EXT_blend_minmax
	- GL_EXT_blend_subtract
	- GL_EXT_blend_color
	- GL_EXT_abgr
	- GL_EXT_texture3D
	- GL_EXT_clip_volume_hint
	- GL_EXT_compiled_vertex_array
	- GL_SGIS_texture_edge_clamp
	- GL_SGIS_generate_mipmap
	- GL_EXT_draw_range_elements
	- GL_SGIS_texture_lod
	- GL_EXT_rescale_normal
	- GL_EXT_packed_pixels
	- GL_EXT_texture_edge_clamp
	- GL_EXT_separate_specular_color
	- GL_ARB_multitexture
	- GL_ARB_map_buffer_alignment
	- GL_ARB_conservative_depth
	- GL_EXT_texture_env_combine
	- GL_EXT_bgra
	- GL_EXT_blend_func_separate
	- GL_EXT_secondary_color
	- GL_EXT_fog_coord
	- GL_EXT_texture_env_add
	- GL_ARB_texture_cube_map
	- GL_ARB_transpose_matrix
	- GL_ARB_internalformat_query
	- GL_ARB_internalformat_query2
	- GL_ARB_texture_env_add
	- GL_IBM_texture_mirrored_repeat
	- GL_ARB_texture_mirrored_repeat
	- GL_EXT_multi_draw_arrays
	- GL_SUN_multi_draw_arrays
	- GL_NV_blend_square
	- GL_ARB_texture_compression
	- GL_3DFX_texture_compression_FXT1
	- GL_EXT_texture_filter_anisotropic
	- GL_ARB_texture_border_clamp
	- GL_ARB_point_parameters
	- GL_ARB_texture_env_combine
	- GL_ARB_texture_env_dot3
	- GL_ARB_texture_env_crossbar
	- GL_EXT_texture_compression_s3tc
	- GL_ARB_shadow
	- GL_ARB_window_pos
	- GL_EXT_shadow_funcs
	- GL_EXT_stencil_wrap
	- GL_ARB_vertex_program
	- GL_EXT_texture_rectangle
	- GL_ARB_fragment_program
	- GL_EXT_stencil_two_side
	- GL_ATI_separate_stencil
	- GL_ARB_vertex_buffer_object
	- GL_EXT_texture_lod_bias
	- GL_ARB_occlusion_query
	- GL_ARB_fragment_shader
	- GL_ARB_shader_objects
	- GL_ARB_shading_language_100
	- GL_ARB_texture_non_power_of_two
	- GL_ARB_vertex_shader
	- GL_NV_texgen_reflection
	- GL_ARB_point_sprite
	- GL_ARB_fragment_program_shadow
	- GL_EXT_blend_equation_separate
	- GL_ARB_depth_texture
	- GL_ARB_texture_rectangle
	- GL_ARB_draw_buffers
	- GL_ARB_color_buffer_float
	- GL_ARB_half_float_pixel
	- GL_ARB_texture_float
	- GL_ARB_pixel_buffer_object
	- GL_ARB_texture_barrier
	- GL_EXT_framebuffer_object
	- GL_ARB_draw_instanced
	- GL_ARB_half_float_vertex
	- GL_ARB_occlusion_query2
	- GL_EXT_draw_buffers2
	- GL_WIN_swap_hint
	- GL_EXT_texture_sRGB
	- GL_ARB_multisample
	- GL_EXT_packed_float
	- GL_EXT_texture_shared_exponent
	- GL_ARB_texture_rg
	- GL_ARB_texture_compression_rgtc
	- GL_NV_conditional_render
	- GL_ARB_texture_swizzle
	- GL_EXT_texture_swizzle
	- GL_ARB_texture_gather
	- GL_ARB_sync
	- GL_ARB_cl_event
	- GL_ARB_framebuffer_sRGB
	- GL_EXT_packed_depth_stencil
	- GL_ARB_depth_buffer_float
	- GL_EXT_transform_feedback
	- GL_ARB_transform_feedback2
	- GL_ARB_draw_indirect
	- GL_EXT_framebuffer_blit
	- GL_EXT_framebuffer_multisample
	- GL_ARB_framebuffer_object
	- GL_ARB_framebuffer_no_attachments
	- GL_EXT_texture_array
	- GL_EXT_texture_integer
	- GL_ARB_map_buffer_range
	- GL_ARB_texture_buffer_range
	- GL_EXT_texture_snorm
	- GL_ARB_blend_func_extended
	- GL_INTEL_performance_query
	- GL_ARB_copy_buffer
	- GL_ARB_sampler_objects
	- GL_NV_primitive_restart
	- GL_ARB_seamless_cube_map
	- GL_ARB_seamless_cubemap_per_texture
	- GL_ARB_uniform_buffer_object
	- GL_ARB_depth_clamp
	- GL_ARB_vertex_array_bgra
	- GL_ARB_shader_bit_encoding
	- GL_ARB_draw_buffers_blend
	- GL_ARB_geometry_shader4
	- GL_EXT_geometry_shader4
	- GL_ARB_texture_query_lod
	- GL_ARB_explicit_attrib_location
	- GL_ARB_draw_elements_base_vertex
	- GL_EXT_shader_integer_mix
	- GL_ARB_instanced_arrays
	- GL_ARB_base_instance
	- GL_ARB_fragment_coord_conventions
	- GL_EXT_gpu_program_parameters
	- GL_ARB_texture_buffer_object_rgb32
	- GL_ARB_compatibility
	- GL_ARB_texture_rgb10_a2ui
	- GL_ARB_texture_multisample
	- GL_ARB_vertex_type_2_10_10_10_rev
	- GL_ARB_vertex_type_10f_11f_11f_rev
	- GL_ARB_timer_query
	- GL_EXT_timer_query
	- GL_ARB_tessellation_shader
	- GL_ARB_vertex_array_object
	- GL_ARB_provoking_vertex
	- GL_ARB_sample_shading
	- GL_ARB_texture_cube_map_array
	- GL_EXT_gpu_shader4
	- GL_ARB_gpu_shader5
	- GL_ARB_gpu_shader_fp64
	- GL_INTEL_fragment_shader_ordering
	- GL_ARB_fragment_shader_interlock
	- GL_ARB_clip_control
	- GL_EXT_shader_framebuffer_fetch
	- GL_ARB_shader_subroutine
	- GL_ARB_transform_feedback3
	- GL_ARB_get_program_binary
	- GL_ARB_separate_shader_objects
	- GL_ARB_shader_precision
	- GL_ARB_vertex_attrib_64bit
	- GL_ARB_viewport_array
	- GL_ARB_transform_feedback_instanced
	- GL_ARB_compressed_texture_pixel_storage
	- GL_ARB_shader_atomic_counters
	- GL_ARB_shading_language_packing
	- GL_ARB_shader_image_load_store
	- GL_ARB_shading_language_420pack
	- GL_ARB_texture_storage
	- GL_EXT_texture_storage
	- GL_ARB_compute_shader
	- GL_ARB_vertex_attrib_binding
	- GL_ARB_texture_view
	- GL_ARB_fragment_layer_viewport
	- GL_ARB_multi_draw_indirect
	- GL_ARB_program_interface_query
	- GL_ARB_shader_image_size
	- GL_ARB_shader_storage_buffer_object
	- GL_ARB_texture_storage_multisample
	- GL_ARB_buffer_storage
	- GL_AMD_vertex_shader_layer
	- GL_AMD_vertex_shader_viewport_index
	- GL_ARB_query_buffer_object
	- GL_EXT_polygon_offset_clamp
	- GL_ARB_clear_texture
	- GL_ARB_texture_mirror_clamp_to_edge
	- GL_ARB_debug_output
	- GL_ARB_enhanced_layouts
	- GL_KHR_debug
	- GL_ARB_arrays_of_arrays
	- GL_ARB_texture_query_levels
	- GL_ARB_invalidate_subdata
	- GL_ARB_clear_buffer_object
	- GL_AMD_depth_clamp_separate
	- GL_ARB_shader_stencil_export
	- GL_INTEL_map_texture
	- GL_ARB_texture_compression_bptc
	- GL_ARB_ES2_compatibility
	- GL_ARB_ES3_compatibility
	- GL_ARB_robustness
	- GL_ARB_robust_buffer_access_behavior
	- GL_EXT_texture_sRGB_decode
	- GL_KHR_texture_compression_astc_ldr
	- GL_KHR_texture_compression_astc_hdr
	- GL_ARB_copy_image
	- GL_KHR_blend_equation_advanced
	- GL_EXT_direct_state_access
	- GL_ARB_stencil_texturing
	- GL_ARB_texture_stencil8
	- GL_ARB_explicit_uniform_location
	- GL_INTEL_multi_rate_fragment_shader
	- GL_ARB_multi_bind
	- GL_ARB_indirect_parameters
	- WGL_EXT_depth_float
	- WGL_ARB_buffer_region
	- WGL_ARB_extensions_string
	- WGL_ARB_make_current_read
	- WGL_ARB_pixel_format
	- WGL_ARB_pbuffer
	- WGL_EXT_extensions_string
	- WGL_EXT_swap_control
	- WGL_EXT_swap_control_tear
	- WGL_ARB_multisample
	- WGL_ARB_pixel_format_float
	- WGL_ARB_framebuffer_sRGB
	- WGL_ARB_create_context
	- WGL_ARB_create_context_profile
	- WGL_EXT_pixel_format_packed_float
	- WGL_EXT_create_context_es_profile
	- WGL_EXT_create_context_es2_profile
	- WGL_NV_DX_interop
	- WGL_INTEL_cl_sharing
	- WGL_NV_DX_interop2
	- WGL_ARB_create_context_robustness

Here is the OpenCL report of GPU Caps Viewer:

- CL_PLATFORM_NAME: Intel(R) OpenCL
- CL_PLATFORM_VENDOR: Intel(R) Corporation
- CL_PLATFORM_VERSION: OpenCL 2.0 
- CL_PLATFORM_PROFILE: FULL_PROFILE
- Num devices: 2
	- CL_DEVICE_NAME: Intel(R) HD Graphics 530
	- CL_DEVICE_VENDOR: Intel(R) Corporation
	- CL_DRIVER_VERSION: 20.19.15.4501
	- CL_DEVICE_PROFILE: FULL_PROFILE
	- CL_DEVICE_VERSION: OpenCL 2.0 
	- CL_DEVICE_TYPE: GPU
	- CL_DEVICE_VENDOR_ID: 0x8086
	- CL_DEVICE_MAX_COMPUTE_UNITS: 24
	- CL_DEVICE_MAX_CLOCK_FREQUENCY: 1150MHz
	- CL_DEVICE_ADDRESS_BITS: 32
	- CL_DEVICE_MAX_MEM_ALLOC_SIZE: 381133KB
	- CL_DEVICE_GLOBAL_MEM_SIZE: 1488MB
	- CL_DEVICE_MAX_PARAMETER_SIZE: 1024
	- CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE: 64 Bytes
	- CL_DEVICE_GLOBAL_MEM_CACHE_SIZE: 512KB
	- CL_DEVICE_ERROR_CORRECTION_SUPPORT: NO
	- CL_DEVICE_LOCAL_MEM_TYPE: Local (scratchpad)
	- CL_DEVICE_LOCAL_MEM_SIZE: 64KB
	- CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE: 64KB
	- CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS: 3
	- CL_DEVICE_MAX_WORK_ITEM_SIZES: [256 ; 256 ; 256]
	- CL_DEVICE_MAX_WORK_GROUP_SIZE: 256
	- CL_EXEC_NATIVE_KERNEL: 19808432
	- CL_DEVICE_IMAGE_SUPPORT: YES
	- CL_DEVICE_MAX_READ_IMAGE_ARGS: 128
	- CL_DEVICE_MAX_WRITE_IMAGE_ARGS: 128
	- CL_DEVICE_IMAGE2D_MAX_WIDTH: 16384
	- CL_DEVICE_IMAGE2D_MAX_HEIGHT: 16384
	- CL_DEVICE_IMAGE3D_MAX_WIDTH: 16384
	- CL_DEVICE_IMAGE3D_MAX_HEIGHT: 16384
	- CL_DEVICE_IMAGE3D_MAX_DEPTH: 2048
	- CL_DEVICE_MAX_SAMPLERS: 16
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE: 0
	- CL_DEVICE_EXTENSIONS: 28
	- Extensions:
		- cl_intel_accelerator
		- cl_intel_advanced_motion_estimation
		- cl_intel_ctz
		- cl_intel_d3d11_nv12_media_sharing
		- cl_intel_dx9_media_sharing
		- cl_intel_motion_estimation
		- cl_intel_simultaneous_sharing
		- cl_intel_subgroups
		- cl_khr_3d_image_writes
		- cl_khr_byte_addressable_store
		- cl_khr_d3d10_sharing
		- cl_khr_d3d11_sharing
		- cl_khr_depth_images
		- cl_khr_dx9_media_sharing
		- cl_khr_fp16
		- cl_khr_gl_depth_images
		- cl_khr_gl_event
		- cl_khr_gl_msaa_sharing
		- cl_khr_global_int32_base_atomics
		- cl_khr_global_int32_extended_atomics
		- cl_khr_gl_sharing
		- cl_khr_icd
		- cl_khr_image2d_from_buffer
		- cl_khr_local_int32_base_atomics
		- cl_khr_local_int32_extended_atomics
		- cl_khr_mipmap_image
		- cl_khr_mipmap_image_writes
		- cl_khr_spir

	- CL_DEVICE_NAME: Intel(R) Core(TM) i5-6600K CPU @ 3.50GHz
	- CL_DEVICE_VENDOR: Intel(R) Corporation
	- CL_DRIVER_VERSION: 5.2.0.10094
	- CL_DEVICE_PROFILE: FULL_PROFILE
	- CL_DEVICE_VERSION: OpenCL 2.0 (Build 10094)
	- CL_DEVICE_TYPE: CPU
	- CL_DEVICE_VENDOR_ID: 0x8086
	- CL_DEVICE_MAX_COMPUTE_UNITS: 4
	- CL_DEVICE_MAX_CLOCK_FREQUENCY: 3500MHz
	- CL_DEVICE_ADDRESS_BITS: 32
	- CL_DEVICE_MAX_MEM_ALLOC_SIZE: 524256KB
	- CL_DEVICE_GLOBAL_MEM_SIZE: 2047MB
	- CL_DEVICE_MAX_PARAMETER_SIZE: 3840
	- CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE: 64 Bytes
	- CL_DEVICE_GLOBAL_MEM_CACHE_SIZE: 256KB
	- CL_DEVICE_ERROR_CORRECTION_SUPPORT: NO
	- CL_DEVICE_LOCAL_MEM_TYPE: Global
	- CL_DEVICE_LOCAL_MEM_SIZE: 32KB
	- CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE: 128KB
	- CL_DEVICE_MAX_WORK_ITEM_DIMENSIONS: 3
	- CL_DEVICE_MAX_WORK_ITEM_SIZES: [8192 ; 8192 ; 8192]
	- CL_DEVICE_MAX_WORK_GROUP_SIZE: 8192
	- CL_EXEC_NATIVE_KERNEL: 19808428
	- CL_DEVICE_IMAGE_SUPPORT: YES
	- CL_DEVICE_MAX_READ_IMAGE_ARGS: 480
	- CL_DEVICE_MAX_WRITE_IMAGE_ARGS: 480
	- CL_DEVICE_IMAGE2D_MAX_WIDTH: 16384
	- CL_DEVICE_IMAGE2D_MAX_HEIGHT: 16384
	- CL_DEVICE_IMAGE3D_MAX_WIDTH: 2048
	- CL_DEVICE_IMAGE3D_MAX_HEIGHT: 2048
	- CL_DEVICE_IMAGE3D_MAX_DEPTH: 2048
	- CL_DEVICE_MAX_SAMPLERS: 480
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_CHAR: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_SHORT: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT: 1
	- CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE: 1
	- CL_DEVICE_EXTENSIONS: 16
	- Extensions:
		- cl_khr_icd
		- cl_khr_global_int32_base_atomics
		- cl_khr_global_int32_extended_atomics
		- cl_khr_local_int32_base_atomics
		- cl_khr_local_int32_extended_atomics
		- cl_khr_byte_addressable_store
		- cl_khr_depth_images
		- cl_khr_3d_image_writes
		- cl_intel_exec_by_local_thread
		- cl_khr_spir
		- cl_khr_dx9_media_sharing
		- cl_intel_dx9_media_sharing
		- cl_khr_d3d11_sharing
		- cl_khr_gl_sharing
		- cl_khr_fp64
		- cl_khr_image2d_from_buffer

Posted in Download, News | Tagged Broadwell, Driver, GPU, Haswell, Intel, Skylake

GPU Caps Viewer 1.34.0 Released

Published May 6, 2017

A new version of GPU Caps Viewer (OpenGL, Vulkan, OpenCL and CUDA utility) is available.

GPU Caps Viewer 1.34.0 Released

1 – Overview

GPU Caps Viewer 1.34.0 adds the support of the latest GeForce GTX 1080 Ti, Radeon RX 580, RX 570 and RX 560 (based on Polaris 10/11 GPUs) as well as Radeon Pro WX 7100, WX 5100 and WX 4100.

New Vulkan and OpenGL demos (based on GeeXLab engine) have been added in the 3D demos panel:

GPU Caps Viewer 1.34 + new GeeXLab demos

All GeeXLab demos can now be launched using the command line:



GpuCapsViewer.exe /demo_win_width=1920 /demo_win_height=1080

/demo_fullscreen /run_gxl_demo=vk_shadertoy_geomechanical

/benchmark_log_results /benchmark_duration=10000 /demo_msaa=0

/vk_gpu_index=0

Command line parameters can be found in the _run_geexlab_benchmark.bat file available in GPU Caps Viewer folder. The benchmark results are saved in a CSV file (_gxl_benchmark_results.csv).

Another change brought to GPU Caps concerns the UAC (User Account Control) execution level that has been reset to the default value (as invoker). The previous UAC level (administrator) didn’t work well with launching demos via the command line.

GPU Caps Viewer 1.33 + GeForce GTX 1060

All Vulkan code has been recompiled with latest Vulkan headers (v1.0.45).

>> Update: 2017.04.29 <<

GPU Caps Viewer 1.34.3.1 is a maintenance release and comes with a new VK-Z version. VS2015 generates some strange patterns in the 32-bit version of VK-Z and no matter the way I compile VK-Z, the 32-bit version of VK-Z is still flagged as infected by some antivirus. The 64-bit version of VK-Z is clean so I just replaced the 32-bit version by the 64-bit one. GPU Caps Viewer has been updated to execute VK-Z x64 on Windows 64-bit only…

>> Update: 2017.04.26 <<

GPU Caps Viewer 1.34.3.0 is a maintenance release and improves the detection of recent AMD Radeon GPUs (RX 580, RX 570, RX 480 and RX 470). GPU Shark has been updated to version 0.9.11.4. VK-Z has been updated to version 0.6.0.5 32-bit (it has been recompiled with other compilation options in order to avoid false positive detection with antivirus. I really don’t understand this pesky problem!).

>> Update: 2017.04.16 <<

GPU Caps Viewer 1.34.2.1 is available with VK-Z 0.6.0.3. The previous 32-bit version of VK-Z was detected as infected by some antivirus scanners. Now VK-Z 32-bit is clean for Nod32, Avira, Avast, Kaspersky, AVG, Norton, and MacAfee.

>> Update: 2017.04.14 <<

GPU Caps Viewer 1.34.2.0 adds the support of NVIDIA TITAN Xp. GPU Shark and VK-Z have been updated to their latest versions.

>> Update: 2017.04.01 <<

GPU Caps Viewer 1.34.1.0 improves the detection of AMD Radeon GPUs in some systems that include Intel iGPU + AMD Radeon GPU. A new GeeXLab/OpenGL demo has been added (Alien Corridor).

GPU Caps Viewer 1.34.1

2 – Dowloads

2.1 – Portable version (zip archive – no installation required):

2.2 – Win32 installer:

For any feedback or bug report, a thread on Geeks3D forums is available HERE.

3 – What is GPU Caps Viewer?

GPU Caps Viewer is a graphics card information utility focused on the OpenGL, Vulkan, OpenCL and CUDA API level support of the main (primary) graphics card. For Vulkan, OpenCL and CUDA, GPU Caps Viewer details the API support of each capable device available in the system. GPU Caps Viewer offers also a simple GPU monitoring facility (clock speed, temperature, GPU usage, fan speed) for NVIDIA GeForce and AMD Radeon based graphics cards. GPU data can be submitted to an online GPU database.

4 – Changelog

Version 1.34.3.1 – 2017.04.29
! replaced VK-Z 0.6.0.5 32-bit by VK-Z 0.6.0.3 64-bit.
VK-Z 64-bit does not produce false positive with some
antivirus. VK-Z 64-bit is only executed on Windows 64-bit.

Version 1.34.3.0 – 2017.04.20
! improved detection of Radeon RX 580, RX 570,
RX 480 and RX 470.
! updated: GPU Shark 0.9.11.4
! updated: VK-Z 0.6.0.5
! updated: ZoomGPU 1.20.4 (GPU monitoring library).

Version 1.34.2.1 – 2017.04.16
! updated: VK-Z 0.6.0.3

Version 1.34.2.0 – 2017.04.14
+ added support of NVIDIA TITAN Xp.
! updated Radeon RX 560 shader cores.
! updated: VK-Z 0.6.0
! updated with GeeXLab SDK libs.
! updated: GPU Shark 0.9.11.3
! updated: ZoomGPU 1.20.3 (GPU monitoring library).

Version 1.34.1.0 – 2017.04.01
! improves the detection of AMD Radeon GPUs in some systems
that include Intel iGPU + AMD Radeon GPU.
+ added new OpenGL demo (GeeXLab): Alien Corridor (based on this demo).
Command line demo codename: gl21_shadertoy_mp_alien_corridor
! updated: GPU Shark 0.9.11.2
! updated: ZoomGPU 1.20.2 (GPU monitoring library).

Version 1.34.0.0 – 2017.03.25
+ added support of the GeForce GTX 1080 Ti.
+ added support of AMD Radeon RX 580, RX 570 and RX 560.
+ added support of AMD Radeon Pro WX 7100, WX 5100, WX4100,
WX 4150 and WX 4130.
+ added initial support of AMD Polaris 12 and Vega 10 based videocards.
+ added new parameters for launching GeeXLab demos via the command line.
+ added new Vulkan and OpenGL demos (GeeXLab): Vulkan geomechanical (based on this demo), OpenGL rainforest (based on this demo), OpenGL radialblur (based on this demo), OpenGL rhodium (based on this demo), OpenGL cell shading, OpenGL geometry instancing, OpenGL gs mesh exploder.
! set UAC (User Account Control) execution level to as invoker.
! recompiled with latest Vulkan API headers (v1.0.45).
! updated with latest GeeXLab SDK libs.
! updated: GPU Shark 0.9.11.1
! updated: ZoomGPU 1.20.1 (GPU monitoring library).

Posted in Download, News | Tagged CUDA, GPU, OpenCL, OpenGL, Software, Vulkan

NVIDIA TITAN Xp vs TITAN X

Published May 6, 2017

NVIDIA TITAN Xp

NVIDIA has more or less silently launched a new high end graphics card around 10 days ago. Here are some pictures of the TITAN Xp versus the TITAN X (launched last year), both gaming cards being based on a Pascal GP102 GPU…

The TITAN Xp is based on a full GP102 GPU with 3840 CUDA cores while the TITAN X has only 3584 CUDA cores. The TITAN Xp has 240 texture units while the TITAN X has 224 texture units. Both cards have the same number of ROPs (96) and the same amount of memory: 12GB of GDDR5X.

On the physical side, there are few differences between the TITAN Xp and the TITAN X. NVIDIA has not changed the name on the VGA shroud: TITAN X for both cards…

NVIDIA TITAN Xp

The only things that distinguish both cards are the box, the PCB color (brown for the Xp) and the DVI connector (the Xp has no DVI connector):

NVIDIA TITAN Xp vs TITAN X

The TITAN Xp alone:

NVIDIA TITAN Xp

Posted in News | Tagged Compare, CUDA, GPU, Nvidia, Pascal, TITAN

FurMark 1.19.0 Released

Published May 6, 2017

FurMark, stress test session

A maintenance release of FurMark, the popular GPU stress test utility, is available.

1 – Release highlights

FurMark 1.19.0 adds the support of recent NVIDIA GPUs (GeForce GTX 1080 Ti, TITAN Xp) as well as AMD Radeon RX 500 series (RX 580 and RX 570). The GPU monitoring library has been updated with the latest iteration of the NVAPI and GPU Shark + GPU-Z have been updated to their latest versions.

FurMark, stress test session

2 – Download

You can download FurMark from the following link:

Posted in Download, News | Tagged FurMark, GeForce, GPU, Radeon, TITAN

GPUs Help Cut Siri’s Error Rate by Half

Published September 22, 2016

GPUs Help Cut Siri’s Error Rate by Half

To make Siri great, Apple employed several artificial intelligence experts three years ago to apply deep learning to their intelligent mobile smart assistant.

The team began training a neural net to replace the original Siri. “We have the biggest and baddest GPU farm cranking all the time,” says Alex Acero, who heads the speech team.

“The error rate has been cut by a factor of two in all the languages, more than a factor of two in many cases,” says Acero. “That’s mostly due to deep learning and the way we have optimized it.”

Apple Siri GPU

Besides Siri, Apple’s adoption of deep learning and neural nets are now found all over their products and services — including fraud detection on the Apple store, facial recognition and locations in your photos, and to help identify the most useful feedback from thousands of reports from beta testers.

“The typical customer is going to experience deep learning on a day-to-day level that [exemplifies] what you love about an Apple product,” says Phil Schiller, senior vice president of worldwide marketing at Apple. “The most exciting [instances] are so subtle that you don’t even think about it until the third time you see it, and then you stop and say, How is this happening?”

Posted in Artificial Intelligence | Tagged Artificial intelligence, Deep Learning, GPU, Image Recognition, Machine learning

GPU-Accelerated PC Solves Complex Problems Hundreds of Times Faster Than Massive CPU-only Supercomputers

Published August 14, 2016

GPU-Accelerated PC Solves Complex Problems Hundreds of Times Faster Than Massive CPU-only Supercomputers

Russian scientists from Lomonosov Moscow State University used an ordinary GPU-accelerated desktop computer to solve complex quantum mechanics equations in just 15 minutes that would typically take two to three days on a large CPU-only supercomputer.

Senior researchers Vladimir Pomerantcev and Olga Rubtsova and professor Vladimir Kukulin used a GeForce GTX 670 with CUDA and the PGI CUDA Fortran Compiler to calculate equations formulated in the ‘60s by Russian mathematician Ludwig Faddeev that describe the scattering patterns of quantum particles. The approach described in the group’s research paper is based on a complete discretization of few-particle continuum and uses massively parallel computations to spread the calculations across thousands of different streams to successfully solve the problem on a single GPU.

“We reached the speed we couldn’t even dream of,” Kukulin said. “The program computes 260 million of complex double integrals on a desktop computer within three seconds only. No comparison with supercomputers! My colleague from the University of Bochum in Germany, whose lab did the same, carried out the calculations by one of the largest supercomputers in Germany that is actually very expensive. And what his group is seeking for two or three days, we do in 15 minutes without spending a dime.”

GPU-Accelerated PC Solves Complex Problems Hundreds of Times Faster Than Massive CPU-only Supercomputers

This work opened new ways for the researchers to analyze nuclear reactions that could help solve large computing tasks in the field of plasma physics, geophysics, medicine, and many other areas of science.

Posted in News | Tagged CPU, GPU, GTX 670, Numerical Techniques, Physics, Processing

Advanced Real-Time Visualization for Robotic Heart Surgery

Published August 14, 2016

Advanced Real-Time Visualization for Robotic Heart Surgery

Researchers at the Harvard Biorobotics Laboratory are harnessing the power of GPUs to generate real-time volumetric renderings of patients’ hearts. The team has built a robotic system to autonomously steer commercially available cardiac catheters that can acquire ultrasound images from within the heart. They tested their system in the clinic and reported their results at the 2016 IEEE International Conference on Robotics and Automation (ICRA) in Stockholm, Sweden.

The team used an Intracardiac Echocardiography (ICE) catheter, which is equipped with an ultrasound transducer at the tip, to acquire 2D images from within a beating heart. Using NVIDIA GPUs, the team was able to reconstruct a 4D (3D + time) model of the heart from these ultrasound images.

Generating a 4D volume begins with co-registering ultrasound images that are acquired at different imaging angles but at the same phase of the cardiac cycle. The position and rotation of each image with respect to the world coordinate frame is measured using electromagnetic (EM) trackers that are attached to the catheter body. This point cloud is then discretized to lie on a 3D grid. Next, infilling is performed to fill the gaps between the slices, generating a dense volumetric representation of the heart. Finally, the volumes are displayed to the surgeon using volume rendering via raycasting, leveraging the CUDA – OpenGL interoperability. The team accelerated the volume reconstruction and rendering algorithms using two NVIDIA TITAN GPUs.

“ICE catheters are currently seldom used due to the difficulty in manual steering,” said principal investigator Prof. Robert D. Howe, Abbott and James Lawrence Professor of Engineering at Harvard University. “Our robotic system frees the clinicians of this burden, and presents them with a new method of real-time visualization that is safer and higher quality than the X-ray imaging that is used in the clinic. This is an enabling technology that can lead to new procedures that were not possible before, as well as improving the efficacy of the current ones.”

Providing real-time procedure guidance requires the use of efficient algorithms combined with a high-performance computing platform. Images are acquired at up to 60 frames per second from the ultrasound machine. Generating volumetric renderings from these images in real-time is only possible using GPUs.

Posted in Artificial Intelligence, Virtual Reality | Tagged CUDA, GeForce, GPU, IEEE, Life Sciences, Medical Sciences, Nvidia, Robotics, virtual reality

Open-Access Visual Search Tool for Satellite Imagery

Published June 5, 2016

Open-Access Visual Search Tool for Satellite Imagery

A new project by Carnegie Mellon University researchers provides journalists, citizen scientists, and other researchers with the ability to quickly scan large geographical regions for specific visual features.

Simply click on a feature in the satellite imagery – a baseball diamond, cul-de-sac, tennis court – and Terrapattern will find other things that look similar in the area and pinpoint them on the map.

Using a deep learning neural network trained for five days on an NVIDIA GeForce GPU, their model will look at small squares of the landscape and, comparing those patterns to a huge database of tagged map features from OpenStreetMap, it learned to associate them with certain concepts.

Open-Access Visual Search Tool for Satellite Imagery

Currently, Terrapattern is limited to Pittsburgh, San Francisco, New York City and Detroit, but access to more cities is coming soon.

Posted in Artificial Intelligence, Machine learning | Tagged Artificial intelligence, GeForce, GPU, Higher Education, Image Processing, Machine learning, Nvidia

GPU-Accelerated Model Reveals Details of Nuclear Fission

Published June 5, 2016

GPU-Accelerated Model Reveals Details of Nuclear Fission

Scientists from University of Washington, Warsaw University of Technology in Poland, Pacific Northwest National Laboratory, and Los Alamos National Laboratory, have developed a model that provides a detailed look at what happens during the last stages of the fission process.

According to their research paper, nuclear fission has almost reached the venerable age of 80 years and yet we still lack an understanding in terms of a fully quantum microscopic approach.

Using the new model, the scientists determined that fission fragments remain connected far longer than expected before the daughter nuclei split apart and the predicted kinetic energy agrees with results from experimental observations. This discovery indicates that complex calculations of real-time fission dynamics without physical restrictions are feasible and opens a pathway to a theoretical microscopic framework with abundant predictive power.

Snapshots of the total density profile of the 240Pu fission process.

Evaluating the theory amounted to solving about 56,000 complex coupled nonlinear, time-dependent, three-dimensional partial differential equations for a ²⁴⁰Pu nucleus using a highly efficient parallelized GPU code. The calculations required nearly 2,000 NVIDIA GPUs on the Titan supercomputer at Oak Ridge National Lab.

By accurately modeling fission dynamics, the work will impact research areas such as future reactor fuel compositions, nuclear forensics, and studies of nuclear reactions.

Posted in Artificial Intelligence | Tagged GPU, Life Sciences, Medical Sciences, Nuclear Fission, Nvidia, Tesla

Virtual 3D Teleportation in Real Time with NVIDIA GPUs

Published June 5, 2016

Virtual 3D Teleportation in Real Time with NVIDIA GPUs

Imagine being able to virtually teleport from one space to another in real time.

With 3D-capture cameras and a mixed reality display such as HoloLens, Microsoft Research’s new ‘haloportation’ innovation allows users to see, hear, and interact with remote participants in 3D as if they are actually present in the same physical space.

Custom software helps reconstruct every possibly viewpoint from each camera and then stitches them together into one fully formed 3D model.

Virtual 3D Teleportation in Real Time with NVIDIA GPUs

“We want to do all of this processing in a tiny window — around 33 milliseconds — to process all the data coming from all of the cameras at once, basically, and also create a temporal model, and then stream the data,” says project lead Shahram Izadi, whose team leans on NVIDIA GPUs to crunch the relevant numbers.

Izadi also mentioned the system is able to record and playback entire previous ‘haloportation’ sessions which is like walking back through time to experience memorable events.

Posted in News, Virtual Reality | Tagged Artificial intelligence, GPU, Machine learning, Nvidia, Virtual 3D, virtual reality

: Ranked 5th – Competition 2016 : Brick and Mortar Store Recommendation with Budget Constraints

: Special Prize – Competition 2015 : The Purchase and Redemption Forecasts

: Design an algorithm to improve edges and image enhancement for under-sea color images in Persian Gulf

: Survey Mothers of children 9 to 14 in Isfahan on a computer game with content concepts of citizenship

: An overview of the designing new engines to create video games

: Investigate new real-time shading techniques in virtual reality and video games

: Real Time Quality Control of National Monitoring and Forecasting Network

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: 2nd International Confrence on knowledge-based Research in Computer Engineering and IT-Optimizing raytracing algorithm using CUDA

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