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  • Will Intel be releasing anymore 6-core processors soon?

    - by jasondavis
    I am about to start buying parts every week for as long as it takes me to build the best PC I can build. I am looking at the Intel i7-920 processor right now because it is about 250$ and it is a quad-core processor based on the x58 chipset I believe. From what I have read so far, intel is coming out with some 6-core processors soon that will also use the x58 chipset and will allow me to use the same motherboard and memory/ram to upgrade to a 6-core. This sounds really good to me right now. I just read that the new 6-core processor. The Core i7-980X (extreme edition) was just released which is the first 6-core processor but it is supposed to be around $1,000 so I will probably just get the i7-920 for now and then upgrade to the 6-core version when the price goes down. The motherboard I am looking at getting the GIGABYTE GA-X58A-UD5 which is around $280 at newegg.com So that is my basic plan SO far. I have not purchased any parts yet. I am just wanting to ask if this sounds like a good idea or if I should wait longer if I am wanting to eventually have a 6-core processor. Does anyone know if Intel is planning on releasing any other 6-core processor in addition to the Core i7-980X in the near future? I just want to make sure I am buying the best setup for my money if I am going all out on it, thanks for any tips/advice.

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  • how many processors can I get in a block on cuda GPU?

    - by Vickey
    hi all, I have two questions to ask 1) If I create only one block of threads in cuda and execute the my parallel program on it then is it possible that more than one processors would be given to single block so that my program get some benefit of multiprocessor platform ? 2) can I synchronize the threads of different blocks ? if yes please give some hints. Thanks in advance since I know I'll get replies as always I get.

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  • django/python: is one view that handles two sibling models a good idea?

    - by clime
    I am using django multi-table inheritance: Video and Image are models derived from Media. I have implemented two views: video_list and image_list, which are just proxies to media_list. media_list returns images or videos (based on input parameter model) for a certain object, which can be of type Event, Member, or Crag. The view alters its behaviour based on input parameter action (better name would be mode), which can be of value "edit" or "view". The problem is that I need to ask whether the input parameter model contains Video or Image in media_list so that I can do the right thing. Similar condition is also in helper method media_edit_list that is called from the view. I don't particularly like it but the only alternative I can think of is to have separate (but almost the same) logic for video_list and image_list and then probably also separate helper methods for videos and images: video_edit_list, image_edit_list, video_view_list, image_view_list. So four functions instead of just two. That I like even less because the video functions would be very similar to the respective image functions. What do you recommend? Here is extract of relevant parts: http://pastebin.com/07t4bdza. I'll also paste the code here: #urls url(r'^media/images/(?P<rel_model_tag>(event|member|crag))/(?P<rel_object_id>\d+)/(?P<action>(view|edit))/$', views.image_list, name='image-list') url(r'^media/videos/(?P<rel_model_tag>(event|member|crag))/(?P<rel_object_id>\d+)/(?P<action>(view|edit))/$', views.video_list, name='video-list') #views def image_list(request, rel_model_tag, rel_object_id, mode): return media_list(request, Image, rel_model_tag, rel_object_id, mode) def video_list(request, rel_model_tag, rel_object_id, mode): return media_list(request, Video, rel_model_tag, rel_object_id, mode) def media_list(request, model, rel_model_tag, rel_object_id, mode): rel_model = tag_to_model(rel_model_tag) rel_object = get_object_or_404(rel_model, pk=rel_object_id) if model == Image: star_media = rel_object.star_image else: star_media = rel_object.star_video filter_params = {} if rel_model == Event: filter_params['event'] = rel_object_id elif rel_model == Member: filter_params['members'] = rel_object_id elif rel_model == Crag: filter_params['crag'] = rel_object_id media_list = model.objects.filter(~Q(id=star_media.id)).filter(**filter_params).order_by('date_added').all() context = { 'media_list': media_list, 'star_media': star_media, } if mode == 'edit': return media_edit_list(request, model, rel_model_tag, rel_object_id, context) return media_view_list(request, model, rel_model_tag, rel_object_id, context) def media_view_list(request, model, rel_model_tag, rel_object_id, context): if request.is_ajax(): context['base_template'] = 'boxes/base-lite.html' return render(request, 'media/list-items.html', context) def media_edit_list(request, model, rel_model_tag, rel_object_id, context): if model == Image: get_media_edit_record = get_image_edit_record else: get_media_edit_record = get_video_edit_record media_list = [get_media_edit_record(media, rel_model_tag, rel_object_id) for media in context['media_list']] if context['star_media']: star_media = get_media_edit_record(context['star_media'], rel_model_tag, rel_object_id) else: star_media = None json = simplejson.dumps({ 'star_media': star_media, 'media_list': media_list, }) return HttpResponse(json, content_type=json_response_mimetype(request)) def get_image_edit_record(image, rel_model_tag, rel_object_id): record = { 'url': image.image.url, 'name': image.title or image.filename, 'type': mimetypes.guess_type(image.image.path)[0] or 'image/png', 'thumbnailUrl': image.thumbnail_2.url, 'size': image.image.size, 'id': image.id, 'media_id': image.media_ptr.id, 'starUrl':reverse('image-star', kwargs={'image_id': image.id, 'rel_model_tag': rel_model_tag, 'rel_object_id': rel_object_id}), } return record def get_video_edit_record(video, rel_model_tag, rel_object_id): record = { 'url': video.embed_url, 'name': video.title or video.url, 'type': None, 'thumbnailUrl': video.thumbnail_2.url, 'size': None, 'id': video.id, 'media_id': video.media_ptr.id, 'starUrl': reverse('video-star', kwargs={'video_id': video.id, 'rel_model_tag': rel_model_tag, 'rel_object_id': rel_object_id}), } return record # models class Media(models.Model, WebModel): title = models.CharField('title', max_length=128, default='', db_index=True, blank=True) event = models.ForeignKey(Event, null=True, default=None, blank=True) crag = models.ForeignKey(Crag, null=True, default=None, blank=True) members = models.ManyToManyField(Member, blank=True) added_by = models.ForeignKey(Member, related_name='added_images') date_added = models.DateTimeField('date added', auto_now_add=True, null=True, default=None, editable=False) class Image(Media): image = ProcessedImageField(upload_to='uploads', processors=[ResizeToFit(width=1024, height=1024, upscale=False)], format='JPEG', options={'quality': 75}) thumbnail_1 = ImageSpecField(source='image', processors=[SmartResize(width=178, height=134)], format='JPEG', options={'quality': 75}) thumbnail_2 = ImageSpecField(source='image', #processors=[SmartResize(width=256, height=192)], processors=[ResizeToFit(height=164)], format='JPEG', options={'quality': 75}) class Video(Media): url = models.URLField('url', max_length=256, default='') embed_url = models.URLField('embed url', max_length=256, default='', blank=True) author = models.CharField('author', max_length=64, default='', blank=True) thumbnail = ProcessedImageField(upload_to='uploads', processors=[ResizeToFit(width=1024, height=1024, upscale=False)], format='JPEG', options={'quality': 75}, null=True, default=None, blank=True) thumbnail_1 = ImageSpecField(source='thumbnail', processors=[SmartResize(width=178, height=134)], format='JPEG', options={'quality': 75}) thumbnail_2 = ImageSpecField(source='thumbnail', #processors=[SmartResize(width=256, height=192)], processors=[ResizeToFit(height=164)], format='JPEG', options={'quality': 75}) class Crag(models.Model, WebModel): name = models.CharField('name', max_length=64, default='', db_index=True) normalized_name = models.CharField('normalized name', max_length=64, default='', editable=False) type = models.IntegerField('crag type', null=True, default=None, choices=crag_types) description = models.TextField('description', default='', blank=True) country = models.ForeignKey('country', null=True, default=None) #TODO: make this not null when db enables it latitude = models.FloatField('latitude', null=True, default=None) longitude = models.FloatField('longitude', null=True, default=None) location_index = FixedCharField('location index', length=24, default='', editable=False, db_index=True) # handled by db, used for marker clustering added_by = models.ForeignKey('member', null=True, default=None) #route_count = models.IntegerField('route count', null=True, default=None, editable=False) date_created = models.DateTimeField('date created', auto_now_add=True, null=True, default=None, editable=False) last_modified = models.DateTimeField('last modified', auto_now=True, null=True, default=None, editable=False) star_image = models.ForeignKey('Image', null=True, default=None, related_name='star_crags', on_delete=models.SET_NULL) star_video = models.ForeignKey('Video', null=True, default=None, related_name='star_crags', on_delete=models.SET_NULL)

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  • django/python: is one view that handles two separate models a good idea?

    - by clime
    I am using django multi-table inheritance: Video and Image are models derived from Media. I have implemented two views: video_list and image_list, which are just proxies to media_list. media_list returns images or videos (based on input parameter model) for a certain object, which can be of type Event, Member, or Crag. It alters its behaviour based on input parameter action, which can be either "edit" or "view". The problem is that I need to ask whether the input parameter model contains Video or Image in media_list so that I can do the right thing. Similar condition is also in helper method media_edit_list that is called from the view. I don't particularly like it but the only alternative I can think of is to have separate logic for video_list and image_list and then probably also separate helper methods for videos and images: video_edit_list, image_edit_list, video_view_list, image_view_list. So four functions instead of just two. That I like even less because the video functions would be very similar to the respective image functions. What do you recommend? Here is extract of relevant parts: http://pastebin.com/07t4bdza. I'll also paste the code here: #urls url(r'^media/images/(?P<rel_model_tag>(event|member|crag))/(?P<rel_object_id>\d+)/(?P<action>(view|edit))/$', views.video_list, name='image-list') url(r'^media/videos/(?P<rel_model_tag>(event|member|crag))/(?P<rel_object_id>\d+)/(?P<action>(view|edit))/$', views.image_list, name='video-list') #views def image_list(request, rel_model_tag, rel_object_id, action): return media_list(request, Image, rel_model_tag, rel_object_id, action) def video_list(request, rel_model_tag, rel_object_id, action): return media_list(request, Video, rel_model_tag, rel_object_id, action) def media_list(request, model, rel_model_tag, rel_object_id, action): rel_model = tag_to_model(rel_model_tag) rel_object = get_object_or_404(rel_model, pk=rel_object_id) if model == Image: star_media = rel_object.star_image else: star_media = rel_object.star_video filter_params = {} if rel_model == Event: filter_params['media__event'] = rel_object_id elif rel_model == Member: filter_params['media__members'] = rel_object_id elif rel_model == Crag: filter_params['media__crag'] = rel_object_id media_list = model.objects.filter(~Q(id=star_media.id)).filter(**filter_params).order_by('media__date_added').all() context = { 'media_list': media_list, 'star_media': star_media, } if action == 'edit': return media_edit_list(request, model, rel_model_tag, rel_model_id, context) return media_view_list(request, model, rel_model_tag, rel_model_id, context) def media_view_list(request, model, rel_model_tag, rel_object_id, context): if request.is_ajax(): context['base_template'] = 'boxes/base-lite.html' return render(request, 'media/list-items.html', context) def media_edit_list(request, model, rel_model_tag, rel_object_id, context): if model == Image: get_media_record = get_image_record else: get_media_record = get_video_record media_list = [get_media_record(media, rel_model_tag, rel_object_id) for media in context['media_list']] if context['star_media']: star_media = get_media_record(star_media, rel_model_tag, rel_object_id) star_media['starred'] = True else: star_media = None json = simplejson.dumps({ 'star_media': star_media, 'media_list': media_list, }) return HttpResponse(json, content_type=json_response_mimetype(request)) # models class Media(models.Model, WebModel): title = models.CharField('title', max_length=128, default='', db_index=True, blank=True) event = models.ForeignKey(Event, null=True, default=None, blank=True) crag = models.ForeignKey(Crag, null=True, default=None, blank=True) members = models.ManyToManyField(Member, blank=True) added_by = models.ForeignKey(Member, related_name='added_images') date_added = models.DateTimeField('date added', auto_now_add=True, null=True, default=None, editable=False) def __unicode__(self): return self.title def get_absolute_url(self): return self.image.url if self.image else self.video.embed_url class Image(Media): image = ProcessedImageField(upload_to='uploads', processors=[ResizeToFit(width=1024, height=1024, upscale=False)], format='JPEG', options={'quality': 75}) thumbnail_1 = ImageSpecField(source='image', processors=[SmartResize(width=178, height=134)], format='JPEG', options={'quality': 75}) thumbnail_2 = ImageSpecField(source='image', #processors=[SmartResize(width=256, height=192)], processors=[ResizeToFit(height=164)], format='JPEG', options={'quality': 75}) class Video(Media): url = models.URLField('url', max_length=256, default='') embed_url = models.URLField('embed url', max_length=256, default='', blank=True) author = models.CharField('author', max_length=64, default='', blank=True) thumbnail = ProcessedImageField(upload_to='uploads', processors=[ResizeToFit(width=1024, height=1024, upscale=False)], format='JPEG', options={'quality': 75}, null=True, default=None, blank=True) thumbnail_1 = ImageSpecField(source='thumbnail', processors=[SmartResize(width=178, height=134)], format='JPEG', options={'quality': 75}) thumbnail_2 = ImageSpecField(source='thumbnail', #processors=[SmartResize(width=256, height=192)], processors=[ResizeToFit(height=164)], format='JPEG', options={'quality': 75}) class Crag(models.Model, WebModel): name = models.CharField('name', max_length=64, default='', db_index=True) normalized_name = models.CharField('normalized name', max_length=64, default='', editable=False) type = models.IntegerField('crag type', null=True, default=None, choices=crag_types) description = models.TextField('description', default='', blank=True) country = models.ForeignKey('country', null=True, default=None) #TODO: make this not null when db enables it latitude = models.FloatField('latitude', null=True, default=None) longitude = models.FloatField('longitude', null=True, default=None) location_index = FixedCharField('location index', length=24, default='', editable=False, db_index=True) # handled by db, used for marker clustering added_by = models.ForeignKey('member', null=True, default=None) #route_count = models.IntegerField('route count', null=True, default=None, editable=False) date_created = models.DateTimeField('date created', auto_now_add=True, null=True, default=None, editable=False) last_modified = models.DateTimeField('last modified', auto_now=True, null=True, default=None, editable=False) star_image = models.OneToOneField('Image', null=True, default=None, related_name='star_crags', on_delete=models.SET_NULL) star_video = models.OneToOneField('Video', null=True, default=None, related_name='star_crags', on_delete=models.SET_NULL)

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  • SPARC T4-4 Beats 8-CPU IBM POWER7 on TPC-H @3000GB Benchmark

    - by Brian
    Oracle's SPARC T4-4 server delivered a world record TPC-H @3000GB benchmark result for systems with four processors. This result beats eight processor results from IBM (POWER7) and HP (x86). The SPARC T4-4 server also delivered better performance per core than these eight processor systems from IBM and HP. Comparisons below are based upon system to system comparisons, highlighting Oracle's complete software and hardware solution. This database world record result used Oracle's Sun Storage 2540-M2 arrays (rotating disk) connected to a SPARC T4-4 server running Oracle Solaris 11 and Oracle Database 11g Release 2 demonstrating the power of Oracle's integrated hardware and software solution. The SPARC T4-4 server based configuration achieved a TPC-H scale factor 3000 world record for four processor systems of 205,792 QphH@3000GB with price/performance of $4.10/QphH@3000GB. The SPARC T4-4 server with four SPARC T4 processors (total of 32 cores) is 7% faster than the IBM Power 780 server with eight POWER7 processors (total of 32 cores) on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 36% better in price performance compared to the IBM Power 780 server on the TPC-H @3000GB Benchmark. The SPARC T4-4 server is 29% faster than the IBM Power 780 for data loading. The SPARC T4-4 server is up to 3.4 times faster than the IBM Power 780 server for the Refresh Function. The SPARC T4-4 server with four SPARC T4 processors is 27% faster than the HP ProLiant DL980 G7 server with eight x86 processors on the TPC-H @3000GB benchmark. The SPARC T4-4 server is 52% faster than the HP ProLiant DL980 G7 server for data loading. The SPARC T4-4 server is up to 3.2 times faster than the HP ProLiant DL980 G7 for the Refresh Function. The SPARC T4-4 server achieved a peak IO rate from the Oracle database of 17 GB/sec. This rate was independent of the storage used, as demonstrated by the TPC-H @3000TB benchmark which used twelve Sun Storage 2540-M2 arrays (rotating disk) and the TPC-H @1000TB benchmark which used four Sun Storage F5100 Flash Array devices (flash storage). [*] The SPARC T4-4 server showed linear scaling from TPC-H @1000GB to TPC-H @3000GB. This demonstrates that the SPARC T4-4 server can handle the increasingly larger databases required of DSS systems. [*] The SPARC T4-4 server benchmark results demonstrate a complete solution of building Decision Support Systems including data loading, business questions and refreshing data. Each phase usually has a time constraint and the SPARC T4-4 server shows superior performance during each phase. [*] The TPC believes that comparisons of results published with different scale factors are misleading and discourages such comparisons. Performance Landscape The table lists the leading TPC-H @3000GB results for non-clustered systems. TPC-H @3000GB, Non-Clustered Systems System Processor P/C/T – Memory Composite(QphH) $/perf($/QphH) Power(QppH) Throughput(QthH) Database Available SPARC Enterprise M9000 3.0 GHz SPARC64 VII+ 64/256/256 – 1024 GB 386,478.3 $18.19 316,835.8 471,428.6 Oracle 11g R2 09/22/11 SPARC T4-4 3.0 GHz SPARC T4 4/32/256 – 1024 GB 205,792.0 $4.10 190,325.1 222,515.9 Oracle 11g R2 05/31/12 SPARC Enterprise M9000 2.88 GHz SPARC64 VII 32/128/256 – 512 GB 198,907.5 $15.27 182,350.7 216,967.7 Oracle 11g R2 12/09/10 IBM Power 780 4.1 GHz POWER7 8/32/128 – 1024 GB 192,001.1 $6.37 210,368.4 175,237.4 Sybase 15.4 11/30/11 HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 8/64/128 – 512 GB 162,601.7 $2.68 185,297.7 142,685.6 SQL Server 2008 10/13/10 P/C/T = Processors, Cores, Threads QphH = the Composite Metric (bigger is better) $/QphH = the Price/Performance metric in USD (smaller is better) QppH = the Power Numerical Quantity QthH = the Throughput Numerical Quantity The following table lists data load times and refresh function times during the power run. TPC-H @3000GB, Non-Clustered Systems Database Load & Database Refresh System Processor Data Loading(h:m:s) T4Advan RF1(sec) T4Advan RF2(sec) T4Advan SPARC T4-4 3.0 GHz SPARC T4 04:08:29 1.0x 67.1 1.0x 39.5 1.0x IBM Power 780 4.1 GHz POWER7 05:51:50 1.5x 147.3 2.2x 133.2 3.4x HP ProLiant DL980 G7 2.27 GHz Intel Xeon X7560 08:35:17 2.1x 173.0 2.6x 126.3 3.2x Data Loading = database load time RF1 = power test first refresh transaction RF2 = power test second refresh transaction T4 Advan = the ratio of time to T4 time Complete benchmark results found at the TPC benchmark website http://www.tpc.org. Configuration Summary and Results Hardware Configuration: SPARC T4-4 server 4 x SPARC T4 3.0 GHz processors (total of 32 cores, 128 threads) 1024 GB memory 8 x internal SAS (8 x 300 GB) disk drives External Storage: 12 x Sun Storage 2540-M2 array storage, each with 12 x 15K RPM 300 GB drives, 2 controllers, 2 GB cache Software Configuration: Oracle Solaris 11 11/11 Oracle Database 11g Release 2 Enterprise Edition Audited Results: Database Size: 3000 GB (Scale Factor 3000) TPC-H Composite: 205,792.0 QphH@3000GB Price/performance: $4.10/QphH@3000GB Available: 05/31/2012 Total 3 year Cost: $843,656 TPC-H Power: 190,325.1 TPC-H Throughput: 222,515.9 Database Load Time: 4:08:29 Benchmark Description The TPC-H benchmark is a performance benchmark established by the Transaction Processing Council (TPC) to demonstrate Data Warehousing/Decision Support Systems (DSS). TPC-H measurements are produced for customers to evaluate the performance of various DSS systems. These queries and updates are executed against a standard database under controlled conditions. Performance projections and comparisons between different TPC-H Database sizes (100GB, 300GB, 1000GB, 3000GB, 10000GB, 30000GB and 100000GB) are not allowed by the TPC. TPC-H is a data warehousing-oriented, non-industry-specific benchmark that consists of a large number of complex queries typical of decision support applications. It also includes some insert and delete activity that is intended to simulate loading and purging data from a warehouse. TPC-H measures the combined performance of a particular database manager on a specific computer system. The main performance metric reported by TPC-H is called the TPC-H Composite Query-per-Hour Performance Metric (QphH@SF, where SF is the number of GB of raw data, referred to as the scale factor). QphH@SF is intended to summarize the ability of the system to process queries in both single and multiple user modes. The benchmark requires reporting of price/performance, which is the ratio of the total HW/SW cost plus 3 years maintenance to the QphH. A secondary metric is the storage efficiency, which is the ratio of total configured disk space in GB to the scale factor. Key Points and Best Practices Twelve Sun Storage 2540-M2 arrays were used for the benchmark. Each Sun Storage 2540-M2 array contains 12 15K RPM drives and is connected to a single dual port 8Gb FC HBA using 2 ports. Each Sun Storage 2540-M2 array showed 1.5 GB/sec for sequential read operations and showed linear scaling, achieving 18 GB/sec with twelve Sun Storage 2540-M2 arrays. These were stand alone IO tests. The peak IO rate measured from the Oracle database was 17 GB/sec. Oracle Solaris 11 11/11 required very little system tuning. Some vendors try to make the point that storage ratios are of customer concern. However, storage ratio size has more to do with disk layout and the increasing capacities of disks – so this is not an important metric in which to compare systems. The SPARC T4-4 server and Oracle Solaris efficiently managed the system load of over one thousand Oracle Database parallel processes. Six Sun Storage 2540-M2 arrays were mirrored to another six Sun Storage 2540-M2 arrays on which all of the Oracle database files were placed. IO performance was high and balanced across all the arrays. The TPC-H Refresh Function (RF) simulates periodical refresh portion of Data Warehouse by adding new sales and deleting old sales data. Parallel DML (parallel insert and delete in this case) and database log performance are a key for this function and the SPARC T4-4 server outperformed both the IBM POWER7 server and HP ProLiant DL980 G7 server. (See the RF columns above.) See Also Transaction Processing Performance Council (TPC) Home Page Ideas International Benchmark Page SPARC T4-4 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Sun Storage 2540-M2 Array oracle.com OTN Disclosure Statement TPC-H, QphH, $/QphH are trademarks of Transaction Processing Performance Council (TPC). For more information, see www.tpc.org. SPARC T4-4 205,792.0 QphH@3000GB, $4.10/QphH@3000GB, available 5/31/12, 4 processors, 32 cores, 256 threads; IBM Power 780 QphH@3000GB, 192,001.1 QphH@3000GB, $6.37/QphH@3000GB, available 11/30/11, 8 processors, 32 cores, 128 threads; HP ProLiant DL980 G7 162,601.7 QphH@3000GB, $2.68/QphH@3000GB available 10/13/10, 8 processors, 64 cores, 128 threads.

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  • C/C++/Assembly Programatically detect if hyper-threading is active on Windows, Mac and Linux

    - by HTASSCPP
    I can already correctly detect the number of logical processors correctly on all three of these platforms. To be able to detect the number of physical processors/cores correctly I'll have to detect if hyperthreading is supported AND active (or enabled if you prefer) and if so divide the number of logical processors by 2 to determine the number of physical processors. Perphaps I should provide an example: A quad core Intel CPU's with hyperthreading enabled has 4 physical cores, yet 8 logical processors (hyperthreading creates 4 more logical processors). So my current function would detect 8 instead of the desired 4. My question therefore is if there is a way to detect whether hyperthreading is supported AND ENABLED?

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  • Payment Processors - What do I need to know if I want to accept credit cards on my website?

    - by Michael Pryor
    This question talks about different payment processors and what they cost, but I'm looking for the answer to what do I need to do if I want to accept credit card payments? Assume I need to store credit card numbers for customers, so that the obvious solution of relying on the credit card processor to do the heavy lifting is not available. PCI Data Security, which is apparently the standard for storing credit card info, has a bunch of general requirements, but how does one implement them? And what about the vendors, like Visa, who have their own best practices? Do I need to have keyfob access to the machine? What about physically protecting it from hackers in the building? Or even what if someone got their hands on the backup files with the sql server data files on it? What about backups? Are there other physical copies of that data around? Tip: If you get a merchant account, you should negotiate that they charge you "interchange-plus" instead of tiered pricing. With tiered pricing, they will charge you different rates based on what type of Visa/MC is used -- ie. they charge you more for cards with big rewards attached to them. Interchange plus billing means you only pay the processor what Visa/MC charges them, plus a flat fee. (Amex and Discover charge their own rates directly to merchants, so this doesn't apply to those cards. You'll find Amex rates to be in the 3% range and Discover could be as low as 1%. Visa/MC is in the 2% range). This service is supposed to do the negotiation for you (I haven't used it, this is not an ad, and I'm not affiliated with the website, but this service is greatly needed.) This blog post gives a complete rundown of handling credit cards (specifically for the UK).

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  • HP ML350 G4 - do the XEON processors need heat sink compound?

    - by Golfman
    I pulled the heat sinks off a HP ML350 G4 and there appears to be no heat sink compound between the processor and the heat sink surfaces. It looks like the point at which they make contact is actually metal on the processor which is a good conductor anyway. Perhaps the compound is only needed when the processor has a ceramic top instead of a metal one? Anyone know?

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  • How do I calculate clock speed in multi-core processors?

    - by NReilingh
    Is it correct to say, for example, that a processor with four cores each running at 3GHz is in fact a processor running at 12GHz? I once got into a "Mac vs. PC" argument (which by the way is NOT the focus of this topic... that was back in middle school) with an acquaintance who insisted that Macs were only being advertised as 1Ghz machines because they were dual-processor G4s each running at 500MHz. At the time I knew this to be hogwash for reasons I think are apparent to most people, but I just saw a comment on this website to the effect of "6 cores x 0.2GHz = 1.2Ghz" and that got me thinking again about whether there's a real answer to this. So, this is a more-or-less philosophical/deep technical question about the semantics of clock speed calculation. I see two possibilities: Each core is in fact doing x calculations per second, thus the total number of calculations is x(cores). Clock speed is rather a count of the number of cycles the processor goes through in the space of a second, so as long as all cores are running at the same speed, the speed of each clock cycle stays the same no matter how many cores exist. In other words, Hz = (core1Hz+core2Hz+...)/cores.

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  • How Lenovo x200(x61) tablet is so great for programming? whats up with so low GHz processors for deb

    - by Piddubetskyy
    for best laptop for programming after reading here looks like its Mac vs Lenovo (tablet, because tablet is only why I would choose it over Mac). I do crave that tablet but low speed processor scares me. Intel Core i5 or i7 in Sony Vaio sounds more attractive (2,26 - 3GHz for lower price). Yes, Lenovo can be fast, like x201, but with good specifications its over $2,000 its a little too much. For a lot of development I just don't want to wait every time while program compiles and builds during debugging. I want it fairly fast and smooth. Can anyone advice their experience with Lenovo's tablets?

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  • Sun Fire X4270 M3 SAP Enhancement Package 4 for SAP ERP 6.0 (Unicode) Two-Tier Standard Sales and Distribution (SD) Benchmark

    - by Brian
    Oracle's Sun Fire X4270 M3 server achieved 8,320 SAP SD Benchmark users running SAP enhancement package 4 for SAP ERP 6.0 with unicode software using Oracle Database 11g and Oracle Solaris 10. The Sun Fire X4270 M3 server using Oracle Database 11g and Oracle Solaris 10 beat both IBM Flex System x240 and IBM System x3650 M4 server running DB2 9.7 and Windows Server 2008 R2 Enterprise Edition. The Sun Fire X4270 M3 server running Oracle Database 11g and Oracle Solaris 10 beat the HP ProLiant BL460c Gen8 server using SQL Server 2008 and Windows Server 2008 R2 Enterprise Edition by 6%. The Sun Fire X4270 M3 server using Oracle Database 11g and Oracle Solaris 10 beat Cisco UCS C240 M3 server running SQL Server 2008 and Windows Server 2008 R2 Datacenter Edition by 9%. The Sun Fire X4270 M3 server running Oracle Database 11g and Oracle Solaris 10 beat the Fujitsu PRIMERGY RX300 S7 server using SQL Server 2008 and Windows Server 2008 R2 Enterprise Edition by 10%. Performance Landscape SAP-SD 2-Tier Performance Table (in decreasing performance order). SAP ERP 6.0 Enhancement Pack 4 (Unicode) Results (benchmark version from January 2009 to April 2012) System OS Database Users SAPERP/ECCRelease SAPS SAPS/Proc Date Sun Fire X4270 M3 2xIntel Xeon E5-2690 @2.90GHz 128 GB Oracle Solaris 10 Oracle Database 11g 8,320 20096.0 EP4(Unicode) 45,570 22,785 10-Apr-12 IBM Flex System x240 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 EE DB2 9.7 7,960 20096.0 EP4(Unicode) 43,520 21,760 11-Apr-12 HP ProLiant BL460c Gen8 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 EE SQL Server 2008 7,865 20096.0 EP4(Unicode) 42,920 21,460 29-Mar-12 IBM System x3650 M4 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 EE DB2 9.7 7,855 20096.0 EP4(Unicode) 42,880 21,440 06-Mar-12 Cisco UCS C240 M3 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 DE SQL Server 2008 7,635 20096.0 EP4(Unicode) 41,800 20,900 06-Mar-12 Fujitsu PRIMERGY RX300 S7 2xIntel Xeon E5-2690 @2.90GHz 128 GB Windows Server 2008 R2 EE SQL Server 2008 7,570 20096.0 EP4(Unicode) 41,320 20,660 06-Mar-12 Complete benchmark results may be found at the SAP benchmark website http://www.sap.com/benchmark. Configuration and Results Summary Hardware Configuration: Sun Fire X4270 M3 2 x 2.90 GHz Intel Xeon E5-2690 processors 128 GB memory Sun StorageTek 6540 with 4 * 16 * 300GB 15Krpm 4Gb FC-AL Software Configuration: Oracle Solaris 10 Oracle Database 11g SAP enhancement package 4 for SAP ERP 6.0 (Unicode) Certified Results (published by SAP): Number of benchmark users: 8,320 Average dialog response time: 0.95 seconds Throughput: Fully processed order line: 911,330 Dialog steps/hour: 2,734,000 SAPS: 45,570 SAP Certification: 2012014 Benchmark Description The SAP Standard Application SD (Sales and Distribution) Benchmark is a two-tier ERP business test that is indicative of full business workloads of complete order processing and invoice processing, and demonstrates the ability to run both the application and database software on a single system. The SAP Standard Application SD Benchmark represents the critical tasks performed in real-world ERP business environments. SAP is one of the premier world-wide ERP application providers, and maintains a suite of benchmark tests to demonstrate the performance of competitive systems on the various SAP products. See Also SAP Benchmark Website Sun Fire X4270 M3 Server oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Two-tier SAP Sales and Distribution (SD) standard SAP SD benchmark based on SAP enhancement package 4 for SAP ERP 6.0 (Unicode) application benchmark as of 04/11/12: Sun Fire X4270 M3 (2 processors, 16 cores, 32 threads) 8,320 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, Oracle 11g, Solaris 10, Cert# 2012014. IBM Flex System x240 (2 processors, 16 cores, 32 threads) 7,960 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, DB2 9.7, Windows Server 2008 R2 EE, Cert# 2012016. IBM System x3650 M4 (2 processors, 16 cores, 32 threads) 7,855 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, DB2 9.7, Windows Server 2008 R2 EE, Cert# 2012010. Cisco UCS C240 M3 (2 processors, 16 cores, 32 threads) 7,635 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, SQL Server 2008, Windows Server 2008 R2 DE, Cert# 2012011. Fujitsu PRIMERGY RX300 S7 (2 processors, 16 cores, 32 threads) 7,570 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, SQL Server 2008, Windows Server 2008 R2 EE, Cert# 2012008. HP ProLiant DL380p Gen8 (2 processors, 16 cores, 32 threads) 7,865 SAP SD Users, 2 x 2.90 GHz Intel Xeon E5-2690, 128 GB memory, SQL Server 2008, Windows Server 2008 R2 EE, Cert# 2012012. SAP, R/3, reg TM of SAP AG in Germany and other countries. More info www.sap.com/benchmark

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  • Mastering Multicore

    Researchers find a way to make complex computer simulations run more efficiently on chips with multiple processors Computer simulation - Business - Hardware - Processors - Components

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  • Intel Puts Mobile CPUs on a Diet for Ultra-Thin Laptops

    <b>Hardware Central:</b> "Intel today broadened its number of ultra-low voltage processors (ULV) to include a complete range, from Celeron to Core i7, for the super-thin laptop market. This announcement builds on Intel's January introduction of laptop processors, which included only a few low-end ULV processors."

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  • Best Practice for Context Processors vs. Template Tags?

    - by mawimawi
    In which cases is it better to create template tags (and load them into the template), than creating a context processor (which fills the request automatically)? e.g. I have a dynamic menu that has to be included into all templates, so I'm putting it into my base.html. What is the preferred usage: context processor or custom template tag? And why?

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  • User has many computers, computers have many attributes in different tables, best way to JOIN?

    - by krismeld
    I have a table for users: USERS: ID | NAME | ---------------- 1 | JOHN | 2 | STEVE | a table for computers: COMPUTERS: ID | USER_ID | ------------------ 13 | 1 | 14 | 1 | a table for processors: PROCESSORS: ID | NAME | --------------------------- 27 | PROCESSOR TYPE 1 | 28 | PROCESSOR TYPE 2 | and a table for harddrives: HARDDRIVES: ID | NAME | ---------------------------| 35 | HARDDRIVE TYPE 25 | 36 | HARDDRIVE TYPE 90 | Each computer can have many attributes from the different attributes tables (processors, harddrives etc), so I have intersection tables like this, to link the attributes to the computers: COMPUTER_PROCESSORS: C_ID | P_ID | --------------| 13 | 27 | 13 | 28 | 14 | 27 | COMPUTER_HARDDRIVES: C_ID | H_ID | --------------| 13 | 35 | So user JOHN, with id 1 owns computer 13 and 14. Computer 13 has processor 27 and 28, and computer 13 has harddrive 35. Computer 14 has processor 27 and no harddrive. Given a user's id, I would like to retrieve a list of that user's computers with each computers attributes. I have figured out a query that gives me a somewhat of a result: SELECT computers.id, processors.id AS p_id, processors.name AS p_name, harddrives.id AS h_id, harddrives.name AS h_name, FROM computers JOIN computer_processors ON (computer_processors.c_id = computers.id) JOIN processors ON (processors.id = computer_processors.p_id) JOIN computer_harddrives ON (computer_harddrives.c_id = computers.id) JOIN harddrives ON (harddrives.id = computer_harddrives.h_id) WHERE computers.user_id = 1 Result: ID | P_ID | P_NAME | H_ID | H_NAME | ----------------------------------------------------------- 13 | 27 | PROCESSOR TYPE 1 | 35 | HARDDRIVE TYPE 25 | 13 | 28 | PROCESSOR TYPE 2 | 35 | HARDDRIVE TYPE 25 | But this has several problems... Computer 14 doesnt show up, because it has no harddrive. Can I somehow make an OUTER JOIN to make sure that all computers show up, even if there a some attributes they don't have? Computer 13 shows up twice, with the same harddrive listet for both. When more attributes are added to a computer (like 3 blocks of ram), the number of rows returned for that computer gets pretty big, and it makes it had to sort the result out in application code. Can I somehow make a query, that groups the two returned rows together? Or a query that returns NULL in the h_name column in the second row, so that all values returned are unique? EDIT: What I would like to return is something like this: ID | P_ID | P_NAME | H_ID | H_NAME | ----------------------------------------------------------- 13 | 27 | PROCESSOR TYPE 1 | 35 | HARDDRIVE TYPE 25 | 13 | 28 | PROCESSOR TYPE 2 | 35 | NULL | 14 | 27 | PROCESSOR TYPE 1 | NULL | NULL | Or whatever result that make it easy to turn it into an array like this [13] => [P_NAME] => [0] => PROCESSOR TYPE 1 [1] => PROCESSOR TYPE 2 [H_NAME] => [0] => HARDDRIVE TYPE 25 [14] => [P_NAME] => [0] => PROCESSOR TYPE 1

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  • SQL SERVER – Find Max Worker Count using DMV – 32 Bit and 64 Bit

    - by pinaldave
    During several recent training courses, I found it very interesting that Worker Thread is not quite known to everyone despite the fact that it is a very important feature. At some point in the discussion, one of the attendees mentioned that we can double the Worker Thread if we double the CPU (add the same number of CPU that we have on current system). The same discussion has triggered this quick article. Here is the DMV which can be used to find out Max Worker Count SELECT max_workers_count FROM sys.dm_os_sys_info Let us run the above query on my system and find the results. As my system is 32 bit and I have two CPU, the Max Worker Count is displayed as 512. To address the previous discussion, adding more CPU does not necessarily double the Worker Count. In fact, the logic behind this simple principle is as follows: For x86 (32-bit) upto 4 logical processors  max worker threads = 256 For x86 (32-bit) more than 4 logical processors  max worker threads = 256 + ((# Procs – 4) * 8) For x64 (64-bit) upto 4 logical processors  max worker threads = 512 For x64 (64-bit) more than 4 logical processors  max worker threads = 512+ ((# Procs – 4) * 8) In addition to this, you can configure the Max Worker Thread by using SSMS. Go to Server Node >> Right Click and Select Property >> Select Process and modify setting under Worker Threads. According to Book On Line, the default Worker Thread settings are appropriate for most of the systems. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL System Table, SQL Tips and Tricks, T SQL, Technology Tagged: SQL DMV

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  • Price Drop for Processor based License on Exalytics

    - by Mike.Hallett(at)Oracle-BI&EPM
    ·       33% reduction in the list `per processor` license pricing for the Oracle BI Foundation Suite ·       New capacity-based licensing which allows customers to think big & start small, significantly lowering the entry price point for an Exalytics. Oracle BI Software List Price changes In response to new powerful platforms like the in-memory Oracle Exalytics with 40 cpu cores (counted under Oracle pricing policy as 20 “processors”), the list price of “Oracle BI Foundation Suite” (BIFS) is reduced by 33% from $450K per processor to $300K per processor. Capacity-based licensing on Exalytics (Trusted Partitions) “Capacity-based pricing” for the BIFS, Endeca, Essbase and Times Ten for Exalytics software is now available for Exalytics systems. This is delivered using “Oracle VM” (OVM).  We still ship a full Exalytics machine to all customers, but they may choose to only use and license a subset of the processors installed in the machine.   Customers can license Exalytics software in units of 5 “processors”: 5, 10, 15 or the full capacity 20.   As the customer’s implementation and workload increases, it is a simple matter to license additional processors and, using OVM, make them available to the BI or EPM application. Endeca Information Discovery now available on Exalytics Oracle has also announced the certification of “Oracle Endeca Information Discovery” (EID) on the Exalytics machine.    EID can be licensed alone or in combination with the BIFS & Times Ten for an Exalytics stack, and also participates in the capacity based pricing outlined above.   The Exalytics hardware is the perfect platform for EID, and provides superb power and performance for this in-memory hybrid text-search-analytics.   For more information : Oracle Price lists Oracle Partitioning Policy Discussion by Mark Rittman (Rittman Mead Consulting ltd.) on Oracle Trusted Partitions for Oracle Engineered Systems, Oracle Exalytics and Updated BI Foundation Pricing.

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  • Parallel Computing in .Net 4.0

    - by kaleidoscope
    Technorati Tags: Ram,Parallel Computing in .Net 4.0 Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem: To be run using multiple CPUs A problem is broken into discrete parts that can be solved concurrently Each part is further broken down to a series of instructions Instructions from each part execute simultaneously on different CPUs Parallel Extensions in .NET 4.0 provides a set of libraries and tools to achieve the above mentioned objectives. This supports two paradigms of parallel computing Data Parallelism – This refers to dividing the data across multiple processors for parallel execution.e.g we are processing an array of 1000 elements we can distribute the data between two processors say 500 each. This is supported by the Parallel LINQ (PLINQ) in .NET 4.0 Task Parallelism – This breaks down the program into multiple tasks which can be parallelized and are executed on different processors. This is supported by Task Parallel Library (TPL) in .NET 4.0 A high level view is shown below:

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  • How do I balance program CPU reverse compatibility whist still being able to use cutting edge features?

    - by TheLQ
    As I learn more about C and C++ I'm starting to wonder: How can a compiler use newer features of processors without limiting it just to people with, for example, Intel Core i7's? Think about it: new processors come out every year with lots of new technologies. However you can't just only target them since a significant portion of the market will not upgrade to the latest and greatest processors for a long time. I'm more or less wondering how this is handled in general by C and C++ devs and compilers. Do compilers make code similar to if SSE is supported, do this using it, else do that using the slower way or do developers have to implement their algorithm twice, or what? More or less how do you release software that takes advantage of newer processor technologies while still keeping a low common denominator?

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  • How to setup matlab for parallel processing on Amazon EC2?

    - by JohnIdol
    I just setup a Extra Large Heavy Computation EC2 instance to throw it at my Genetic Algorithms problem, hoping to speed up things. This instance has 8 Intel Xeon processors (around 2.4Ghz each) and 7 Gigs of RAM. On my machine I have an Intel Core Duo, and matlab is able to work with my two cores just fine. On the EC2 instance though, matlab only is capable of detecting 1 out of 8 processors. Obviously the difference is that I have my 2 cores on a single processor, while the EC2 instance has 8 distinct processors. My question is, how do I get matlab to work with those 8 processors? I found this paper, but it seems related to setting up matlab with multiple EC2 instances, which is not my problem. Any help appreciated!

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  • problem disposing class in Dictionary it is Still in the heap memory although using GC.Collect

    - by Bahgat Mashaly
    Hello i have a problem disposing class in Dictionary this is my code private Dictionary<string, MyProcessor> Processors = new Dictionary<string, MyProcessor>(); private void button1_Click(object sender, EventArgs e) { if (!Processors.ContainsKey(textBox1.Text)) { Processors.Add(textBox1.Text, new MyProcessor()); } } private void button2_Click(object sender, EventArgs e) { MyProcessor currnt_processor = Processors[textBox1.Text]; Processors.Remove(textBox2.Text); currnt_processor.Dispose(); currnt_processor = null; GC.Collect(); } public class MyProcessor: IDisposable { private bool isDisposed = false; string x = ""; public MyProcessor() { for (int i = 0; i < 20000; i++) { //this line only to increase the memory usage to know if the class is dispose or not x = x + "gggggggggggg"; } this.Dispose(); GC.SuppressFinalize(this); } public void Dispose() { this.Dispose(true); GC.SuppressFinalize(this); } public void Dispose(bool disposing) { if (!this.isDisposed) { isDisposed = true; this.Dispose(); } } ~MyProcessor() { Dispose(false); } } i use "ANTS Memory Profiler" to monitor heap memory the disposing work only when i remove all keys from dictionary how can i destroy the class from heap memory ? thanks in advance

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  • "Work stealing" vs. "Work shrugging (tm)"?

    - by John
    Why is it that I can find lots of information on "work stealing" and nothing on a "work shrugging(tm)" as a load-balancing strategy? I am surprised because work-stealing seems to me to have an inherent weakness when implementating efficient fine-grained load-balancing. Vis:- Relying on consumer processors to implement distribution (by actively stealing) begs the question of what these processors do when they find no work? None of the work-stealing references and implementations I have come across so far address this issue satisfactorarily for me. They either:- 1) Manage not to disclose what they do with idle processors! [Cilk] (?anyone know?) 2) Have all idle processors sleep and wake periodically and scatter messages to the four winds to see if any work has arrived [e.g. JAWS] (= way too latent & inefficient for me). 3) Assume that it is acceptable to have processors "spinning" looking for work ( = non-starter for me!) Unless anyone thinks there is a solution for this I will move on to consider a "Work Shrugging(tm)" strategy. Having the task-producing processor distribute excess load seems to me inherently capable of a much more efficient implementation. However a quick google didn't show up anything under the heading of "Work Shrugging" so any pointers to prior-art would be welcome. tx Tags I would have added if I was allowed to [work-stealing]

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